Lung cancer is a common type of cancer that causes death if not detected early enough. Doctors use computed tomography (CT) images to diagnose lung cancer. The accuracy of the diagnosis relies highly on the doctor's expertise. Recently, clinical decision support systems based on deep learning valuable recommendations to doctors in their diagnoses. In this paper, we present several deep learning models to detect non-small cell lung cancer in CT images and differentiate its main subtypes namely adenocarcinoma, large cell carcinoma, and squamous cell carcinoma. We adopted standard convolutional neural networks (CNN), visual geometry group-16 (VGG16), and VGG19. Besides, we introduce a variant of the CNN that is augmented with convolutional block attention modules (CBAM). CBAM aims to extract informative features by combining cross-channel and spatial information. We also propose variants of VGG16 and VGG19 that utilize a support vector machine (SVM) at the classification layer instead of SoftMax. We validated all models in this study through extensive experiments on a CT lung cancer dataset. Experimental results show that supplementing CNN with CBAM leads to consistent improvements over vanilla CNN. Results also show that the VGG variants that use the SVM classifier outperform the original VGGs by a significant margin.
In this proposed work, we identified the significant research issues on lung cancer risk factors. Capturing and defining symptoms at an early stage is one of the most difficult phases for patients. Based on the history of patients records, we reviewed a number of current research studies on lung cancer and its various stages. We identified that lung cancer is one of the significant research issues in predicting the early stages of cancer disease. This research aimed to develop a model that can detect lung cancer with a remarkably high level of accuracy using the deep learning approach (convolution neural network). This method considers and resolves significant gaps in previous studies. We compare the accuracy levels and loss values of our model with VGG16, InceptionV3, and Resnet50. We found that our model achieved an accuracy of 94% and a minimum loss of 0.1%. Hence physicians can use our convolution neural network models for predicting lung cancer risk factors in the real world. Moreover, this investigation reveals that squamous cell carcinoma, normal, adenocarcinoma, and large cell carcinoma are the most significant risk factors. In addition, the remaining attributes are also crucial for achieving the best performance.
Lung cancer is one of the leading
causes of mortality in every country, affecting
both men and women. Lung cancer has a low
prognosis, resulting in a high death rate. The
computing sector is fully automating it, and the
medical industry is also automating itself with the
aid of image recognition and data analytics. Lung
cancer is one of the most common diseases for
human beings everywhere throughout the world.
Lung cancer is a disease which arises due to growth
of unwanted tissues in the lung and this growth
which spreads beyond the lung are named as
metastasis which spreads into other parts of the
body.
The objective of our project is to inspect accuracy
ratio of two classifiers which is Support Vector
Machine (SVM), and K Nearest Neighbour
(KNN) on common platform that classify lung
cancer in early stage so that many lives can be
saving. The experimental results show that KNN
gives the best result Than SVM. This report
discusses the Implementation details of our
project.
We have done data preprocessing, data cleaning
and implements machine leaning algorithm for
prediction of lung cancer at early stages through
their symptoms. We have used both classification
algorithms to find or predict the accuracy ratio.
Lung cancer is identified as the most common
cancer in the world that causes death. Early
detection has the ability to reduce deaths by 20%.
In the current clinical process, radiologists use
Computed Tomography (CT) scans to identify
lung cancer in early stages. Radiologists do so by
searching for regions called ‘nodules’, which
correspond to abnormal cell growths. But
identifying process is time consuming, laborious
and depends on the experience of the radiologist.
Hence an intelligent system to automatically
assess whether a patient is prone to have a lung
cancer is a need.
This paper presents a novel method which use
deep learning, namely convolutional neural
networks (CNNs) to identify whether a given CT scan shows evidence of lung cancer or not. The
implementation uses a combination of classical
feature-based candidate detection with modern
deep-learning architectures to generate excellent
results better than either of the methods. The
overall implementation consists of two stages.
Nodule Regions-of-Interest (ROI) extraction and
cancer classification. In nodule ROI extraction
stage, we select top most candidate regions as
nodules. A combination of rule based image
processing method and a 2D CNN was used for
this stage. In the cancer classification stage, we
estimate the malignancy of each nodule regions
and hence label the whole CT scan as cancerous
or non-cancerous. A combination of feature based
eXtreme Gradient Boosting (XGBoost) classifier
and 3D CNN was used for this stage. The LUNA
dataset and LIDC dataset were used for both
training and testing. The results were clearly
demonstrated promising classification
performance. The sensitivity, accuracy and
specificity values obtained for
A deep learning framework for accurate diagnosis of colorectal cancer using h...IJECEIAES
Colorectal cancer (CRC) is one of the most prevalent malignancies worldwide, with high mortality and incidence rates. Early detection of the disease may increase the probability of survival, making it critical to develop effective procedures for precise treatment. In the past few years, there has been an increased use of deep learning techniques in image classification that aid in the detection of various types of cancer. In this study, convolutional neural network (CNN) models were used to classify colorectal cancer into benign and malignant. After applying various data preprocessing techniques to the image dataset, we evaluated our prototypes using three distinct subsets of testing data, representing 20%, 30%, and 40% of the total dataset. Additionally, four pre-trained CNN models (ResNet-18, ResNet-50, GoogLeNet, and MobileNetV2) were trained, and the network architectural techniques were compared by applying the Adam optimizer. Finally, we assessed the performance of algorithms in terms of accuracy, sensitivity, specificity, precision, F1-score, and area under the receiver operating characteristic (ROC) curve (AUC). In this research, deep learning approaches demonstrated high efficacy in accurately diagnosing colorectal cancer. This indicates that these techniques have an important and significant value for advancing medical research.
Cancer is one of the deadliest diseases in the world and is responsible for around 13% of all deaths worldwide.
Cancer incidence rate is growing at an alarming rate in the world. Despite the fact that cancer is
preventable and curable in early stages, the vast majority of patients are diagnosed with cancer very late.
Furthermore, cancer commonly comes back after years of treatment. Therefore, it is of paramount
importance to predict cancer recurrence so that specific treatments can be sought. Nonetheless,
conventional methods of predicting cancer recurrence rely solely on histopathology and the results are not
very reliable. The microarray gene expression technology is a promising technology that couldpredict
cancer recurrence by analyzing the gene expression of sample cells. The microarray technology allows
researchers to examine the expression of thousands of genes simultaneously. This paper describes a stateof-
the-art machine learning based approach called averaged one-dependence estimators with subsumption
resolution to tackle the problem of predicting, from DNA microarray gene expression data, whether a
particular cancer will recur within a specific timeframe, which is usually 5 years. To lower the
computational complexity, we employ an entropy-based geneselection approach to select relevant
prognosticgenes that are directly responsible for recurrence prediction. This proposed system has achieved
an average accuracy of 98.9% in predicting cancer recurrence over 3 datasets. The experimental results
demonstrate the efficacy of our framework.
This document summarizes key aspects of low-dose CT (LDCT) techniques, reading methods, and image interpretation for lung cancer screening. It discusses the LDCT technique, noting parameters like multidetector CT scanners, dose levels around 2 mSv, and iterative reconstruction methods. It also covers reading methods like double reading and CAD to improve nodule detection rates. Further, it distinguishes solid versus subsolid nodule types and challenges in their measurement, given implications for malignancy rates and growth patterns.
Classification techniques using gray level co-occurrence matrix features for ...IJECEIAES
Lung cancer, which causes the majority of fatalities worldwide each year, is one of the deadliest diseases. The survival rate of cancer patients could be improved with better cancer detection methods. Image processing and machine learning have both been used to aid in lung cancer detection, but a method that both increase accuracy and increases a patient’s survival rate has yet to be identified. In an effort to find the most effective method for the accurate lung cancer recognition, this paper analyses and compares several classification algorithms. Lung computed tomography (CT) images are enhanced by removing noise using a median filter. For filtered image, threshold segmentation is used to segment it into distinct parts. From the segmented image different features are extracted using the grey level co-occurrence matrix (GLCM). several classification strategies, including support vector machine (SVM), random forest (RF), k-nearest neighbor (KNN), and decision tree (DT) methods, are used to classify lung images as malignant or normal based on the extracted features. Methods are evaluated based on a number of various performance measures, like accuracy, a precision, the recall, and the F1-Score. Based on the experimental outcomes, SVM outperforms other classification methods in accurately detecting lung cancer with an accuracy of 99.32%.
Techniques for detection of solitary pulmonary nodules in human lung and thei...IJCI JOURNAL
This document summarizes techniques that have been proposed for detecting and classifying solitary pulmonary nodules (SPNs) in human lungs. SPNs can be benign or malignant tumors. Computed tomography (CT) is commonly used for detecting SPNs due to its high resolution. Computer-aided detection (CAD) systems can assist radiologists by speeding up and improving the accuracy of SPN detection and classification. The document reviews various image processing techniques used in CAD systems, including preprocessing methods like filtering, lung segmentation algorithms, candidate nodule detection methods, and techniques for reducing false positives. It provides examples of different filters, segmentation approaches, and detection methods that have been evaluated in previous research to build CAD systems for automated SPN
Automatic Pulmonary Nodule Detection in CT Scans using Xception, Resnet50 and...IRJET Journal
This document summarizes research on using deep learning models to detect pulmonary nodules in CT scans and classify them as benign, malignant, or normal to aid in early diagnosis of lung cancer. The researchers developed their own convolutional neural network (CNN) models and also used pre-trained ResNet50 and Xception models to classify images from the IQ-OTHNCCD lung cancer dataset into three classes. They addressed data imbalances to improve model accuracy. The goal was to build a computer-aided system to help clinicians detect lung cancer earlier through automated nodule classification in CT scans.
In this proposed work, we identified the significant research issues on lung cancer risk factors. Capturing and defining symptoms at an early stage is one of the most difficult phases for patients. Based on the history of patients records, we reviewed a number of current research studies on lung cancer and its various stages. We identified that lung cancer is one of the significant research issues in predicting the early stages of cancer disease. This research aimed to develop a model that can detect lung cancer with a remarkably high level of accuracy using the deep learning approach (convolution neural network). This method considers and resolves significant gaps in previous studies. We compare the accuracy levels and loss values of our model with VGG16, InceptionV3, and Resnet50. We found that our model achieved an accuracy of 94% and a minimum loss of 0.1%. Hence physicians can use our convolution neural network models for predicting lung cancer risk factors in the real world. Moreover, this investigation reveals that squamous cell carcinoma, normal, adenocarcinoma, and large cell carcinoma are the most significant risk factors. In addition, the remaining attributes are also crucial for achieving the best performance.
Lung cancer is one of the leading
causes of mortality in every country, affecting
both men and women. Lung cancer has a low
prognosis, resulting in a high death rate. The
computing sector is fully automating it, and the
medical industry is also automating itself with the
aid of image recognition and data analytics. Lung
cancer is one of the most common diseases for
human beings everywhere throughout the world.
Lung cancer is a disease which arises due to growth
of unwanted tissues in the lung and this growth
which spreads beyond the lung are named as
metastasis which spreads into other parts of the
body.
The objective of our project is to inspect accuracy
ratio of two classifiers which is Support Vector
Machine (SVM), and K Nearest Neighbour
(KNN) on common platform that classify lung
cancer in early stage so that many lives can be
saving. The experimental results show that KNN
gives the best result Than SVM. This report
discusses the Implementation details of our
project.
We have done data preprocessing, data cleaning
and implements machine leaning algorithm for
prediction of lung cancer at early stages through
their symptoms. We have used both classification
algorithms to find or predict the accuracy ratio.
Lung cancer is identified as the most common
cancer in the world that causes death. Early
detection has the ability to reduce deaths by 20%.
In the current clinical process, radiologists use
Computed Tomography (CT) scans to identify
lung cancer in early stages. Radiologists do so by
searching for regions called ‘nodules’, which
correspond to abnormal cell growths. But
identifying process is time consuming, laborious
and depends on the experience of the radiologist.
Hence an intelligent system to automatically
assess whether a patient is prone to have a lung
cancer is a need.
This paper presents a novel method which use
deep learning, namely convolutional neural
networks (CNNs) to identify whether a given CT scan shows evidence of lung cancer or not. The
implementation uses a combination of classical
feature-based candidate detection with modern
deep-learning architectures to generate excellent
results better than either of the methods. The
overall implementation consists of two stages.
Nodule Regions-of-Interest (ROI) extraction and
cancer classification. In nodule ROI extraction
stage, we select top most candidate regions as
nodules. A combination of rule based image
processing method and a 2D CNN was used for
this stage. In the cancer classification stage, we
estimate the malignancy of each nodule regions
and hence label the whole CT scan as cancerous
or non-cancerous. A combination of feature based
eXtreme Gradient Boosting (XGBoost) classifier
and 3D CNN was used for this stage. The LUNA
dataset and LIDC dataset were used for both
training and testing. The results were clearly
demonstrated promising classification
performance. The sensitivity, accuracy and
specificity values obtained for
A deep learning framework for accurate diagnosis of colorectal cancer using h...IJECEIAES
Colorectal cancer (CRC) is one of the most prevalent malignancies worldwide, with high mortality and incidence rates. Early detection of the disease may increase the probability of survival, making it critical to develop effective procedures for precise treatment. In the past few years, there has been an increased use of deep learning techniques in image classification that aid in the detection of various types of cancer. In this study, convolutional neural network (CNN) models were used to classify colorectal cancer into benign and malignant. After applying various data preprocessing techniques to the image dataset, we evaluated our prototypes using three distinct subsets of testing data, representing 20%, 30%, and 40% of the total dataset. Additionally, four pre-trained CNN models (ResNet-18, ResNet-50, GoogLeNet, and MobileNetV2) were trained, and the network architectural techniques were compared by applying the Adam optimizer. Finally, we assessed the performance of algorithms in terms of accuracy, sensitivity, specificity, precision, F1-score, and area under the receiver operating characteristic (ROC) curve (AUC). In this research, deep learning approaches demonstrated high efficacy in accurately diagnosing colorectal cancer. This indicates that these techniques have an important and significant value for advancing medical research.
Cancer is one of the deadliest diseases in the world and is responsible for around 13% of all deaths worldwide.
Cancer incidence rate is growing at an alarming rate in the world. Despite the fact that cancer is
preventable and curable in early stages, the vast majority of patients are diagnosed with cancer very late.
Furthermore, cancer commonly comes back after years of treatment. Therefore, it is of paramount
importance to predict cancer recurrence so that specific treatments can be sought. Nonetheless,
conventional methods of predicting cancer recurrence rely solely on histopathology and the results are not
very reliable. The microarray gene expression technology is a promising technology that couldpredict
cancer recurrence by analyzing the gene expression of sample cells. The microarray technology allows
researchers to examine the expression of thousands of genes simultaneously. This paper describes a stateof-
the-art machine learning based approach called averaged one-dependence estimators with subsumption
resolution to tackle the problem of predicting, from DNA microarray gene expression data, whether a
particular cancer will recur within a specific timeframe, which is usually 5 years. To lower the
computational complexity, we employ an entropy-based geneselection approach to select relevant
prognosticgenes that are directly responsible for recurrence prediction. This proposed system has achieved
an average accuracy of 98.9% in predicting cancer recurrence over 3 datasets. The experimental results
demonstrate the efficacy of our framework.
This document summarizes key aspects of low-dose CT (LDCT) techniques, reading methods, and image interpretation for lung cancer screening. It discusses the LDCT technique, noting parameters like multidetector CT scanners, dose levels around 2 mSv, and iterative reconstruction methods. It also covers reading methods like double reading and CAD to improve nodule detection rates. Further, it distinguishes solid versus subsolid nodule types and challenges in their measurement, given implications for malignancy rates and growth patterns.
Classification techniques using gray level co-occurrence matrix features for ...IJECEIAES
Lung cancer, which causes the majority of fatalities worldwide each year, is one of the deadliest diseases. The survival rate of cancer patients could be improved with better cancer detection methods. Image processing and machine learning have both been used to aid in lung cancer detection, but a method that both increase accuracy and increases a patient’s survival rate has yet to be identified. In an effort to find the most effective method for the accurate lung cancer recognition, this paper analyses and compares several classification algorithms. Lung computed tomography (CT) images are enhanced by removing noise using a median filter. For filtered image, threshold segmentation is used to segment it into distinct parts. From the segmented image different features are extracted using the grey level co-occurrence matrix (GLCM). several classification strategies, including support vector machine (SVM), random forest (RF), k-nearest neighbor (KNN), and decision tree (DT) methods, are used to classify lung images as malignant or normal based on the extracted features. Methods are evaluated based on a number of various performance measures, like accuracy, a precision, the recall, and the F1-Score. Based on the experimental outcomes, SVM outperforms other classification methods in accurately detecting lung cancer with an accuracy of 99.32%.
Techniques for detection of solitary pulmonary nodules in human lung and thei...IJCI JOURNAL
This document summarizes techniques that have been proposed for detecting and classifying solitary pulmonary nodules (SPNs) in human lungs. SPNs can be benign or malignant tumors. Computed tomography (CT) is commonly used for detecting SPNs due to its high resolution. Computer-aided detection (CAD) systems can assist radiologists by speeding up and improving the accuracy of SPN detection and classification. The document reviews various image processing techniques used in CAD systems, including preprocessing methods like filtering, lung segmentation algorithms, candidate nodule detection methods, and techniques for reducing false positives. It provides examples of different filters, segmentation approaches, and detection methods that have been evaluated in previous research to build CAD systems for automated SPN
Automatic Pulmonary Nodule Detection in CT Scans using Xception, Resnet50 and...IRJET Journal
This document summarizes research on using deep learning models to detect pulmonary nodules in CT scans and classify them as benign, malignant, or normal to aid in early diagnosis of lung cancer. The researchers developed their own convolutional neural network (CNN) models and also used pre-trained ResNet50 and Xception models to classify images from the IQ-OTHNCCD lung cancer dataset into three classes. They addressed data imbalances to improve model accuracy. The goal was to build a computer-aided system to help clinicians detect lung cancer earlier through automated nodule classification in CT scans.
IRJET- Intelligent Prediction of Lung Cancer Via MRI Images using Morphologic...IRJET Journal
The document describes a proposed system to intelligently predict lung cancer using MRI images and morphological neural network analysis. The proposed system uses a three-stage approach: preprocessing MRI images, extracting features using wavelet decomposition and normalization, and classifying tissues as normal or abnormal using a morphological neural network with image pruning. This combination of morphological image processing and neural networks is intended to more efficiently classify cancer cells and identify affected regions than previous methods.
Gene expression mining for predicting survivability of patients in earlystage...ijbbjournal
After numerous breakthroughs in medicine, microbiology, and pathology in the past century, lung cancer
still remains as a leading cause of cancer-related death even in the developed countries. Lung cancer
accounts roughly for 30% of all cancer-related deaths in the world. Diagnosis and treatments are still
based on traditional histopathology. It is of paramount importance to predictthe survivability of patients in
early stages oflung cancer so that specific treatments can be sought. Nonetheless, histopathology has been
shown by previous studies to be inadequate in predicting lung cancerdevelopment and clinical outcome.
The microarray technology allows researchers to examine the expression of thousands of genes
simultaneously. This paper describes a state-of-the-art machine learning based approach called averaged
one-dependence estimators with subsumption resolution to tackle the problem of predictingwhether a
patient in early stages of lung cancer will survive by mining DNA microarray gene expression data. To
lower the computational complexity, we employ an entropy-based geneselection approach to select relevant
genes that are directly responsible for lungcancer survivability prognosis. The proposed system has
achieved an average accuracy of 92.31% in predicting lung cancer survivability over 2 independent
datasets. The experimental results provide confirmation that gene expression mining can be used to predict
survivability of patients in earlystages of lung cancer.
Glioblastoma Multiforme Identification from Medical Imaging Using Computer Vi...ijscmcj
A tumor also known as neoplasm is a growth in the abnormal tissue which can be differentiated from the surrounding tissue by its structure. A tumor may lead to cancer, which is a major leading cause of death and responsible for around 13% of all deaths world-wide. Cancer incidence rate is growing at an alarming rate in the world. Great knowledge and experience on radiology are required for accurate tumor detection in medical imaging. Automation of tumor detection is required because there might be a shortage of skilled radiologists at a time of great need. We propose an automatic brain tumor detection and localization framework that can detect and localize brain tumor in magnetic resonance imaging. The proposed brain tumor detection and localization framework comprises five steps: image acquisition, pre-processing, edge detection, modified histogram clustering and morphological operations. After morphological operations, tumors appear as pure white color on pure black backgrounds. We used 50 neuroimages to optimize our system and 100 out-of-sample neuroimages to test our system. The proposed tumor detection and localization system was found to be able to accurately detect and localize brain tumor in magnetic resonance imaging. The preliminary results demonstrate how a simple machine learning classifier with a set of simple image-based features can result in high classification accuracy. The preliminary results also demonstrate the efficacy and efficiency of our five-step brain tumor detection and localization approach and motivate us to extend this framework to detect and localize a variety of other types of tumors in other types of medical imagery.
IRJET- Classification of Cancer of the Lungs using ANN and SVM AlgorithmsIRJET Journal
1) The document compares the performance of artificial neural network (ANN) and support vector machine (SVM) classifiers in classifying lung cancer images.
2) Lung cancer is difficult to diagnose early due to subtle symptoms, and accurate classification of images can help doctors detect it earlier to improve survival rates.
3) The study evaluates the ANN and SVM classifiers on online cancer datasets using metrics like accuracy, sensitivity, specificity, and calculates true/false positives and negatives. The goal is to determine which classifier performs better for lung cancer image classification.
IRJET- Computer Aided Detection Scheme to Improve the Prognosis Assessment of...IRJET Journal
This document describes a computer-aided detection scheme to predict the risk of cancer recurrence in early-stage lung cancer patients after surgery. The scheme uses chest CT images taken before surgery to automatically segment lung tumors and extract morphological and texture-based image features. A naive Bayesian classifier is trained on six image features to predict recurrence risk. A separate artificial neural network classifier is trained on two genomic biomarkers to predict risk. The results from the two classifiers are then combined using a fusion method to produce the overall risk prediction. The goal is to more accurately assess prognosis and help doctors better manage early-stage non-small cell lung cancer patients after surgery.
Evaluation of SVM performance in the detection of lung cancer in marked CT s...nooriasukmaningtyas
This document summarizes a study that evaluated the performance of support vector machines (SVM) in detecting lung cancer using a new computed tomography (CT) scan dataset from Iraq. The dataset contains over 1,100 images from 110 cases classified as normal, benign, or malignant. A computer system was proposed that applied preprocessing techniques like enhancement, segmentation, and feature extraction before using SVM for classification. Different SVM kernels and feature extraction methods were evaluated. The best accuracy achieved on this dataset using this approach was 89.88%.
A comparative analysis of chronic obstructive pulmonary disease using machin...IJECEIAES
Chronic obstructive pulmonary disease (COPD) is a general clinical issue in numerous countries considered the fifth reason for inability and the third reason for mortality on a global scale within 2021. From recent reviews, a deep convolutional neural network (CNN) is used in the primary analysis of the deadly COPD, which uses the computed tomography (CT) images procured from the deep learning tools. Detection and analysis of COPD using several image processing techniques, deep learning models, and machine learning models are notable contributions to this review. This research aims to cover the detailed findings on pulmonary diseases or lung diseases, their causes, and symptoms, which will help treat infections with high performance and a swift response. The articles selected have more than 80% accuracy and are tabulated and analyzed for sensitivity, specificity, and area under the curve (AUC) using different methodologies. This research focuses on the various tools and techniques used in COPD analysis and eventually provides an overview of COPD with coronavirus disease 2019 (COVID-19) symptoms.
This document summarizes imaging techniques for colorectal cancer, including double-contrast barium enema, colonoscopy, and CT colonography (virtual colonoscopy). It discusses the benefits and limitations of each technique. For CT colonography, it reviews literature on its accuracy in detecting polyps and cancers in screening populations. Key factors that influence CT colonography results like bowel preparation, insufflation method, and image viewing technique are also summarized. The document concludes that further standardization of the CT colonography technique and additional large clinical trials are needed to establish it as a screening tool.
Cancer recognition from dna microarray gene expression data using averaged on...IJCI JOURNAL
Cancer is a major leading cause of death and responsible for around 13% of all deaths world-wide. Cancer
incidence rate is growing at an alarming rate in the world. Despite the fact that cancer is preventable and
curable in early stages, the vast majority of patients are diagnosed with cancer very late. Therefore, it is of
paramount importance to prevent and detect cancer early. Nonetheless, conventional methods of detecting
and diagnosing cancer rely solely on skilled physicians, with the help of medical imaging, to detect certain
symptoms that usually appear in the late stages of cancer. The microarray gene expression technology is a
promising technology that can detect cancerous cells in early stages of cancer by analyzing gene
expression of tissue samples. The microarray technology allows researchers to examine the expression of
thousands of genes simultaneously. This paper describes a state-of-the-art machine learning based
approach called averaged one-dependence estimators with subsumption resolution to tackle the problem of
recognizing cancer from DNA microarray gene expression data. To lower the computational complexity
and to increase the generalization capability of the system, we employ an entropy-based geneselection
approach to select relevant gene that are directly responsible for cancer discrimination. This proposed
system has achieved an average accuracy of 98.94% in recognizing and classifyingcancer over 11
benchmark cancer datasets. The experimental results demonstrate the efficacy of our framework.
Computer-aided diagnostic system kinds and pulmonary nodule detection efficacyIJECEIAES
This paper summarizes the literature on computer-aided detection (CAD) systems used to identify and diagnose lung nodules in images obtained with computed tomography (CT) scanners. The importance of developing such systems lies in the fact that the process of manually detecting lung nodules is painstaking and sequential work for radiologists, as it takes a long time. Moreover, the pulmonary nodules have multiple appearances and shapes, and the large number of slices generated by the scanner creates great difficulty in accurately locating the lung nodules. The handcraft nodules detection process can be caused by messing some nodules spicily when these nodules' diameter be less than 10 mm. So, the CAD system is an essential assistant to the radiologist in this case of nodule detection, and it contributed to reducing time consumption in nodules detection; moreover, it applied more accuracy in this field. The objective of this paper is to follow up on current and previous work on lung cancer detection and lung nodule diagnosis. This literature dealt with a group of specialized systems in this field quickly and showed the methods used in them. It dealt with an emphasis on a system based on deep learning involving neural convolution networks.
Brain tumor detection and localization in magnetic resonance imagingijitcs
A tumor also known as neoplasm is a growth in the abnormal tissue which can be differentiated from the
surrounding tissue by its structure. A tumor may lead to cancer, which is a major leading cause of death and
responsible for around 13% of all deaths world-wide. Cancer incidence rate is growing at an alarming rate
in the world. Great knowledge and experience on radiology are required for accurate tumor detection in
medical imaging. Automation of tumor detection is required because there might be a shortage of skilled
radiologists at a time of great need. We propose an automatic brain tumor detectionand localization
framework that can detect and localize brain tumor in magnetic resonance imaging. The proposed brain
tumor detection and localization framework comprises five steps: image acquisition, pre-processing, edge
detection, modified histogram clustering and morphological operations. After morphological operations,
tumors appear as pure white color on pure black backgrounds. We used 50 neuroimages to optimize our
system and 100 out-of-sample neuroimages to test our system. The proposed tumor detection and localization
system was found to be able to accurately detect and localize brain tumor in magnetic resonance imaging.
The preliminary results demonstrate how a simple machine learning classifier with a set of simple
image-based features can result in high classification accuracy. The preliminary results also demonstrate the
efficacy and efficiency of our five-step brain tumor detection and localization approach and motivate us to
extend this framework to detect and localize a variety of other types of tumors in other types of medical
imagery.
Deep learning method for lung cancer identification and classificationIAESIJAI
Lung cancer (LC) is calming many lives and is becoming a serious cause of concern. The detection of LC at an early stage assists the chances of recovery. Accuracy of detection of LC at an early stage can be improved with the help of a convolutional neural network (CNN) based deep learning approach. In this paper, we present two methodologies for Lung cancer detection (LCD) applied on Lung image database consortium (LIDC) and image database resource initiative (IDRI) data sets. Classification of these LC images is carried out using support vector machine (SVM), and deep CNN. The CNN is trained with i) multiple batches and ii) single batch for LC image classification as non cancer and cancer image. All these methods are being implemented in MATLAB. The accuracy of classification obtained by SVM is 65%, whereas deep CNN produced detection accuracy of 80% and 100% respectively for multiple and single batch training. The novelty of our experimentation is near 100% classification accuracy obtained by our deep CNN model when tested on 25 Lung computed tomography (CT) test images each of size 512×512 pixels in less than 20 iterations as compared to the research work carried out by other researchers using cropped LC nodule images.
An Overview: Treatment of Lung Cancer on Researcher Point of ViewEswar Publications
Cancers is defined as the uncontrolled cell divisions. Cell does not grow maturely and destined to uncontrolled cell growth. When these cells of lungs grow uncontrolled it is called lung cancer. Nowadays mortality rate due to lung cancer is increasing day by day. Many treatment and diagnoses are now a day’s available to deal with lung cancer. Here we disused different method for diagnosis the common types of lung cancer Non-Small Cell Lung Cancer, Small Cell Lung Cancer, Small Cell Lung Cancer Limited Stage, Small Cell Lung Cancer - Extensive Stage, Lung Adenocarcinoma, Squamous Cell Carcinoma,Bronchioloalveolar carcinoma (BAC), Metastatic lung cancer.
Circulatingtumordna (Ctdna) in Prostate Cancer: Current Insights and New Pers...daranisaha
Prostate Cancer (PC) is the common tumor in men, which represents one of leading cause of cancer death throughout the world. Most patients were diagnosed too late for curative treatment. So, it is necessary to develop a minimal invasive method to identify novel biomarkers.
Circulatingtumordna (Ctdna) in Prostate Cancer: Current Insights and New Pers...semualkaira
Prostate Cancer (PC) is one of the three most common
cancer type for the estimated new cancer cases and deaths, respectively, among men in worldwide [1]. PC is highly heterogeneous in
terms of the clinical behavior and molecular pathogenesis. There
is a plethora of clinical situations between indolent and aggressive
tumors and within the same setting, particularly in the CastrationResistant Prostate Cancer (CRPC). In addition, tumors with the
same histopathologic grade are often biologically heterogeneous
with different outcome. The remarkable variation in PC clinical
behaviour reflects the broad landscape of molecular alterations
among various prostate tumors and within the same tumor at different stages of disease progression
Circulatingtumordna (Ctdna) in Prostate Cancer: Current Insights and New Pers...semualkaira
Prostate Cancer (PC) is the common tumor in men, which represents one of leading cause of cancer death throughout the world. Most patients were diagnosed too late for curative treatment. So, it is necessary to develop a minimal invasive method to identify novel biomarkers. Currently, plasma DNA has attracted increasing attention as a potential tumor marker...
Circulatingtumordna (Ctdna) in Prostate Cancer: Current Insights and New Pers...ClinicsofOncology
Circulating tumor DNA (ctDNA) has potential as a biomarker for prostate cancer (PC) detection and management. While some studies found no difference in ctDNA levels between PC and benign prostate conditions, more recent studies using sensitive techniques like PCR found higher ctDNA levels in PC patients compared to those with benign hyperplasia or healthy donors. ctDNA analysis allows non-invasive genotyping of tumors through methods like targeted sequencing and holds promise for tracking tumor evolution, treatment response, and resistance. However, standardization of sample collection and analysis techniques is still needed before ctDNA can be widely applied in clinical practice.
Circulatingtumordna (Ctdna) in Prostate Cancer: Current Insights and New Pers...semualkaira
Prostate Cancer (PC) is the common tumor in men, which represents one of leading cause of cancer death throughout the world. Most patients were diagnosed too late for curative treatment. So, it is necessary to develop a minimal invasive method to identify novel biomarkers. Currently, plasma DNA has attracted increasing attention as a potential tumor marker...
Circulatingtumordna (Ctdna) in Prostate Cancer: Current Insights and New Pers...AnonIshanvi
Circulating tumor DNA (ctDNA) has potential as a biomarker for prostate cancer (PC) diagnosis and management. ctDNA is DNA released from tumor cells into the bloodstream and makes up a small fraction of total cell-free DNA. Recent studies show ctDNA levels can distinguish between PC patients and those with benign prostate diseases. Advanced techniques like digital PCR and next-generation sequencing allow sensitive detection and characterization of genetic alterations in ctDNA. Ongoing research aims to validate ctDNA analysis for early cancer detection, predicting tumor behavior, and monitoring treatment response in PC.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
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A tumor also known as neoplasm is a growth in the abnormal tissue which can be differentiated from the
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in the world. Great knowledge and experience on radiology are required for accurate tumor detection in
medical imaging. Automation of tumor detection is required because there might be a shortage of skilled
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framework that can detect and localize brain tumor in magnetic resonance imaging. The proposed brain
tumor detection and localization framework comprises five steps: image acquisition, pre-processing, edge
detection, modified histogram clustering and morphological operations. After morphological operations,
tumors appear as pure white color on pure black backgrounds. We used 50 neuroimages to optimize our
system and 100 out-of-sample neuroimages to test our system. The proposed tumor detection and localization
system was found to be able to accurately detect and localize brain tumor in magnetic resonance imaging.
The preliminary results demonstrate how a simple machine learning classifier with a set of simple
image-based features can result in high classification accuracy. The preliminary results also demonstrate the
efficacy and efficiency of our five-step brain tumor detection and localization approach and motivate us to
extend this framework to detect and localize a variety of other types of tumors in other types of medical
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Cancers is defined as the uncontrolled cell divisions. Cell does not grow maturely and destined to uncontrolled cell growth. When these cells of lungs grow uncontrolled it is called lung cancer. Nowadays mortality rate due to lung cancer is increasing day by day. Many treatment and diagnoses are now a day’s available to deal with lung cancer. Here we disused different method for diagnosis the common types of lung cancer Non-Small Cell Lung Cancer, Small Cell Lung Cancer, Small Cell Lung Cancer Limited Stage, Small Cell Lung Cancer - Extensive Stage, Lung Adenocarcinoma, Squamous Cell Carcinoma,Bronchioloalveolar carcinoma (BAC), Metastatic lung cancer.
Circulatingtumordna (Ctdna) in Prostate Cancer: Current Insights and New Pers...daranisaha
Prostate Cancer (PC) is the common tumor in men, which represents one of leading cause of cancer death throughout the world. Most patients were diagnosed too late for curative treatment. So, it is necessary to develop a minimal invasive method to identify novel biomarkers.
Circulatingtumordna (Ctdna) in Prostate Cancer: Current Insights and New Pers...semualkaira
Prostate Cancer (PC) is one of the three most common
cancer type for the estimated new cancer cases and deaths, respectively, among men in worldwide [1]. PC is highly heterogeneous in
terms of the clinical behavior and molecular pathogenesis. There
is a plethora of clinical situations between indolent and aggressive
tumors and within the same setting, particularly in the CastrationResistant Prostate Cancer (CRPC). In addition, tumors with the
same histopathologic grade are often biologically heterogeneous
with different outcome. The remarkable variation in PC clinical
behaviour reflects the broad landscape of molecular alterations
among various prostate tumors and within the same tumor at different stages of disease progression
Circulatingtumordna (Ctdna) in Prostate Cancer: Current Insights and New Pers...semualkaira
Prostate Cancer (PC) is the common tumor in men, which represents one of leading cause of cancer death throughout the world. Most patients were diagnosed too late for curative treatment. So, it is necessary to develop a minimal invasive method to identify novel biomarkers. Currently, plasma DNA has attracted increasing attention as a potential tumor marker...
Circulatingtumordna (Ctdna) in Prostate Cancer: Current Insights and New Pers...ClinicsofOncology
Circulating tumor DNA (ctDNA) has potential as a biomarker for prostate cancer (PC) detection and management. While some studies found no difference in ctDNA levels between PC and benign prostate conditions, more recent studies using sensitive techniques like PCR found higher ctDNA levels in PC patients compared to those with benign hyperplasia or healthy donors. ctDNA analysis allows non-invasive genotyping of tumors through methods like targeted sequencing and holds promise for tracking tumor evolution, treatment response, and resistance. However, standardization of sample collection and analysis techniques is still needed before ctDNA can be widely applied in clinical practice.
Circulatingtumordna (Ctdna) in Prostate Cancer: Current Insights and New Pers...semualkaira
Prostate Cancer (PC) is the common tumor in men, which represents one of leading cause of cancer death throughout the world. Most patients were diagnosed too late for curative treatment. So, it is necessary to develop a minimal invasive method to identify novel biomarkers. Currently, plasma DNA has attracted increasing attention as a potential tumor marker...
Circulatingtumordna (Ctdna) in Prostate Cancer: Current Insights and New Pers...AnonIshanvi
Circulating tumor DNA (ctDNA) has potential as a biomarker for prostate cancer (PC) diagnosis and management. ctDNA is DNA released from tumor cells into the bloodstream and makes up a small fraction of total cell-free DNA. Recent studies show ctDNA levels can distinguish between PC patients and those with benign prostate diseases. Advanced techniques like digital PCR and next-generation sequencing allow sensitive detection and characterization of genetic alterations in ctDNA. Ongoing research aims to validate ctDNA analysis for early cancer detection, predicting tumor behavior, and monitoring treatment response in PC.
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Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Neural network optimizer of proportional-integral-differential controller par...IJECEIAES
Wide application of proportional-integral-differential (PID)-regulator in industry requires constant improvement of methods of its parameters adjustment. The paper deals with the issues of optimization of PID-regulator parameters with the use of neural network technology methods. A methodology for choosing the architecture (structure) of neural network optimizer is proposed, which consists in determining the number of layers, the number of neurons in each layer, as well as the form and type of activation function. Algorithms of neural network training based on the application of the method of minimizing the mismatch between the regulated value and the target value are developed. The method of back propagation of gradients is proposed to select the optimal training rate of neurons of the neural network. The neural network optimizer, which is a superstructure of the linear PID controller, allows increasing the regulation accuracy from 0.23 to 0.09, thus reducing the power consumption from 65% to 53%. The results of the conducted experiments allow us to conclude that the created neural superstructure may well become a prototype of an automatic voltage regulator (AVR)-type industrial controller for tuning the parameters of the PID controller.
An improved modulation technique suitable for a three level flying capacitor ...IJECEIAES
This research paper introduces an innovative modulation technique for controlling a 3-level flying capacitor multilevel inverter (FCMLI), aiming to streamline the modulation process in contrast to conventional methods. The proposed
simplified modulation technique paves the way for more straightforward and
efficient control of multilevel inverters, enabling their widespread adoption and
integration into modern power electronic systems. Through the amalgamation of
sinusoidal pulse width modulation (SPWM) with a high-frequency square wave
pulse, this controlling technique attains energy equilibrium across the coupling
capacitor. The modulation scheme incorporates a simplified switching pattern
and a decreased count of voltage references, thereby simplifying the control
algorithm.
A review on features and methods of potential fishing zoneIJECEIAES
This review focuses on the importance of identifying potential fishing zones in seawater for sustainable fishing practices. It explores features like sea surface temperature (SST) and sea surface height (SSH), along with classification methods such as classifiers. The features like SST, SSH, and different classifiers used to classify the data, have been figured out in this review study. This study underscores the importance of examining potential fishing zones using advanced analytical techniques. It thoroughly explores the methodologies employed by researchers, covering both past and current approaches. The examination centers on data characteristics and the application of classification algorithms for classification of potential fishing zones. Furthermore, the prediction of potential fishing zones relies significantly on the effectiveness of classification algorithms. Previous research has assessed the performance of models like support vector machines, naïve Bayes, and artificial neural networks (ANN). In the previous result, the results of support vector machine (SVM) were 97.6% more accurate than naive Bayes's 94.2% to classify test data for fisheries classification. By considering the recent works in this area, several recommendations for future works are presented to further improve the performance of the potential fishing zone models, which is important to the fisheries community.
Electrical signal interference minimization using appropriate core material f...IJECEIAES
As demand for smaller, quicker, and more powerful devices rises, Moore's law is strictly followed. The industry has worked hard to make little devices that boost productivity. The goal is to optimize device density. Scientists are reducing connection delays to improve circuit performance. This helped them understand three-dimensional integrated circuit (3D IC) concepts, which stack active devices and create vertical connections to diminish latency and lower interconnects. Electrical involvement is a big worry with 3D integrates circuits. Researchers have developed and tested through silicon via (TSV) and substrates to decrease electrical wave involvement. This study illustrates a novel noise coupling reduction method using several electrical involvement models. A 22% drop in electrical involvement from wave-carrying to victim TSVs introduces this new paradigm and improves system performance even at higher THz frequencies.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
Enhancing battery system identification: nonlinear autoregressive modeling fo...IJECEIAES
Precisely characterizing Li-ion batteries is essential for optimizing their
performance, enhancing safety, and prolonging their lifespan across various
applications, such as electric vehicles and renewable energy systems. This
article introduces an innovative nonlinear methodology for system
identification of a Li-ion battery, employing a nonlinear autoregressive with
exogenous inputs (NARX) model. The proposed approach integrates the
benefits of nonlinear modeling with the adaptability of the NARX structure,
facilitating a more comprehensive representation of the intricate
electrochemical processes within the battery. Experimental data collected
from a Li-ion battery operating under diverse scenarios are employed to
validate the effectiveness of the proposed methodology. The identified
NARX model exhibits superior accuracy in predicting the battery's behavior
compared to traditional linear models. This study underscores the
importance of accounting for nonlinearities in battery modeling, providing
insights into the intricate relationships between state-of-charge, voltage, and
current under dynamic conditions.
Smart grid deployment: from a bibliometric analysis to a surveyIJECEIAES
Smart grids are one of the last decades' innovations in electrical energy.
They bring relevant advantages compared to the traditional grid and
significant interest from the research community. Assessing the field's
evolution is essential to propose guidelines for facing new and future smart
grid challenges. In addition, knowing the main technologies involved in the
deployment of smart grids (SGs) is important to highlight possible
shortcomings that can be mitigated by developing new tools. This paper
contributes to the research trends mentioned above by focusing on two
objectives. First, a bibliometric analysis is presented to give an overview of
the current research level about smart grid deployment. Second, a survey of
the main technological approaches used for smart grid implementation and
their contributions are highlighted. To that effect, we searched the Web of
Science (WoS), and the Scopus databases. We obtained 5,663 documents
from WoS and 7,215 from Scopus on smart grid implementation or
deployment. With the extraction limitation in the Scopus database, 5,872 of
the 7,215 documents were extracted using a multi-step process. These two
datasets have been analyzed using a bibliometric tool called bibliometrix.
The main outputs are presented with some recommendations for future
research.
Use of analytical hierarchy process for selecting and prioritizing islanding ...IJECEIAES
One of the problems that are associated to power systems is islanding
condition, which must be rapidly and properly detected to prevent any
negative consequences on the system's protection, stability, and security.
This paper offers a thorough overview of several islanding detection
strategies, which are divided into two categories: classic approaches,
including local and remote approaches, and modern techniques, including
techniques based on signal processing and computational intelligence.
Additionally, each approach is compared and assessed based on several
factors, including implementation costs, non-detected zones, declining
power quality, and response times using the analytical hierarchy process
(AHP). The multi-criteria decision-making analysis shows that the overall
weight of passive methods (24.7%), active methods (7.8%), hybrid methods
(5.6%), remote methods (14.5%), signal processing-based methods (26.6%),
and computational intelligent-based methods (20.8%) based on the
comparison of all criteria together. Thus, it can be seen from the total weight
that hybrid approaches are the least suitable to be chosen, while signal
processing-based methods are the most appropriate islanding detection
method to be selected and implemented in power system with respect to the
aforementioned factors. Using Expert Choice software, the proposed
hierarchy model is studied and examined.
Enhancing of single-stage grid-connected photovoltaic system using fuzzy logi...IJECEIAES
The power generated by photovoltaic (PV) systems is influenced by
environmental factors. This variability hampers the control and utilization of
solar cells' peak output. In this study, a single-stage grid-connected PV
system is designed to enhance power quality. Our approach employs fuzzy
logic in the direct power control (DPC) of a three-phase voltage source
inverter (VSI), enabling seamless integration of the PV connected to the
grid. Additionally, a fuzzy logic-based maximum power point tracking
(MPPT) controller is adopted, which outperforms traditional methods like
incremental conductance (INC) in enhancing solar cell efficiency and
minimizing the response time. Moreover, the inverter's real-time active and
reactive power is directly managed to achieve a unity power factor (UPF).
The system's performance is assessed through MATLAB/Simulink
implementation, showing marked improvement over conventional methods,
particularly in steady-state and varying weather conditions. For solar
irradiances of 500 and 1,000 W/m2
, the results show that the proposed
method reduces the total harmonic distortion (THD) of the injected current
to the grid by approximately 46% and 38% compared to conventional
methods, respectively. Furthermore, we compare the simulation results with
IEEE standards to evaluate the system's grid compatibility.
Enhancing photovoltaic system maximum power point tracking with fuzzy logic-b...IJECEIAES
Photovoltaic systems have emerged as a promising energy resource that
caters to the future needs of society, owing to their renewable, inexhaustible,
and cost-free nature. The power output of these systems relies on solar cell
radiation and temperature. In order to mitigate the dependence on
atmospheric conditions and enhance power tracking, a conventional
approach has been improved by integrating various methods. To optimize
the generation of electricity from solar systems, the maximum power point
tracking (MPPT) technique is employed. To overcome limitations such as
steady-state voltage oscillations and improve transient response, two
traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb
and observe (P&O), have been modified. This research paper aims to
simulate and validate the step size of the proposed modified P&O and FLC
techniques within the MPPT algorithm using MATLAB/Simulink for
efficient power tracking in photovoltaic systems.
Adaptive synchronous sliding control for a robot manipulator based on neural ...IJECEIAES
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
robot hands is always an attractive topic in the research community. This is a
challenging problem because robot manipulators are complex nonlinear systems
and are often subject to fluctuations in loads and external disturbances. This
article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller
ensures that the positions of the joints track the desired trajectory, synchronize
the errors, and significantly reduces chattering. First, the synchronous tracking
errors and synchronous sliding surfaces are presented. Second, the synchronous
tracking error dynamics are determined. Third, a robust adaptive control law is
designed,the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy
logic. The built algorithm ensures that the tracking and approximation errors
are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results.
Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is
significantly reduced.
Remote field-programmable gate array laboratory for signal acquisition and de...IJECEIAES
A remote laboratory utilizing field-programmable gate array (FPGA) technologies enhances students’ learning experience anywhere and anytime in embedded system design. Existing remote laboratories prioritize hardware access and visual feedback for observing board behavior after programming, neglecting comprehensive debugging tools to resolve errors that require internal signal acquisition. This paper proposes a novel remote embeddedsystem design approach targeting FPGA technologies that are fully interactive via a web-based platform. Our solution provides FPGA board access and debugging capabilities beyond the visual feedback provided by existing remote laboratories. We implemented a lab module that allows users to seamlessly incorporate into their FPGA design. The module minimizes hardware resource utilization while enabling the acquisition of a large number of data samples from the signal during the experiments by adaptively compressing the signal prior to data transmission. The results demonstrate an average compression ratio of 2.90 across three benchmark signals, indicating efficient signal acquisition and effective debugging and analysis. This method allows users to acquire more data samples than conventional methods. The proposed lab allows students to remotely test and debug their designs, bridging the gap between theory and practice in embedded system design.
Detecting and resolving feature envy through automated machine learning and m...IJECEIAES
Efficiently identifying and resolving code smells enhances software project quality. This paper presents a novel solution, utilizing automated machine learning (AutoML) techniques, to detect code smells and apply move method refactoring. By evaluating code metrics before and after refactoring, we assessed its impact on coupling, complexity, and cohesion. Key contributions of this research include a unique dataset for code smell classification and the development of models using AutoGluon for optimal performance. Furthermore, the study identifies the top 20 influential features in classifying feature envy, a well-known code smell, stemming from excessive reliance on external classes. We also explored how move method refactoring addresses feature envy, revealing reduced coupling and complexity, and improved cohesion, ultimately enhancing code quality. In summary, this research offers an empirical, data-driven approach, integrating AutoML and move method refactoring to optimize software project quality. Insights gained shed light on the benefits of refactoring on code quality and the significance of specific features in detecting feature envy. Future research can expand to explore additional refactoring techniques and a broader range of code metrics, advancing software engineering practices and standards.
Smart monitoring technique for solar cell systems using internet of things ba...IJECEIAES
Rapidly and remotely monitoring and receiving the solar cell systems status parameters, solar irradiance, temperature, and humidity, are critical issues in enhancement their efficiency. Hence, in the present article an improved smart prototype of internet of things (IoT) technique based on embedded system through NodeMCU ESP8266 (ESP-12E) was carried out experimentally. Three different regions at Egypt; Luxor, Cairo, and El-Beheira cities were chosen to study their solar irradiance profile, temperature, and humidity by the proposed IoT system. The monitoring data of solar irradiance, temperature, and humidity were live visualized directly by Ubidots through hypertext transfer protocol (HTTP) protocol. The measured solar power radiation in Luxor, Cairo, and El-Beheira ranged between 216-1000, 245-958, and 187-692 W/m 2 respectively during the solar day. The accuracy and rapidity of obtaining monitoring results using the proposed IoT system made it a strong candidate for application in monitoring solar cell systems. On the other hand, the obtained solar power radiation results of the three considered regions strongly candidate Luxor and Cairo as suitable places to build up a solar cells system station rather than El-Beheira.
An efficient security framework for intrusion detection and prevention in int...IJECEIAES
Over the past few years, the internet of things (IoT) has advanced to connect billions of smart devices to improve quality of life. However, anomalies or malicious intrusions pose several security loopholes, leading to performance degradation and threat to data security in IoT operations. Thereby, IoT security systems must keep an eye on and restrict unwanted events from occurring in the IoT network. Recently, various technical solutions based on machine learning (ML) models have been derived towards identifying and restricting unwanted events in IoT. However, most ML-based approaches are prone to miss-classification due to inappropriate feature selection. Additionally, most ML approaches applied to intrusion detection and prevention consider supervised learning, which requires a large amount of labeled data to be trained. Consequently, such complex datasets are impossible to source in a large network like IoT. To address this problem, this proposed study introduces an efficient learning mechanism to strengthen the IoT security aspects. The proposed algorithm incorporates supervised and unsupervised approaches to improve the learning models for intrusion detection and mitigation. Compared with the related works, the experimental outcome shows that the model performs well in a benchmark dataset. It accomplishes an improved detection accuracy of approximately 99.21%.
Batteries -Introduction – Types of Batteries – discharging and charging of battery - characteristics of battery –battery rating- various tests on battery- – Primary battery: silver button cell- Secondary battery :Ni-Cd battery-modern battery: lithium ion battery-maintenance of batteries-choices of batteries for electric vehicle applications.
Fuel Cells: Introduction- importance and classification of fuel cells - description, principle, components, applications of fuel cells: H2-O2 fuel cell, alkaline fuel cell, molten carbonate fuel cell and direct methanol fuel cells.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
A SYSTEMATIC RISK ASSESSMENT APPROACH FOR SECURING THE SMART IRRIGATION SYSTEMSIJNSA Journal
The smart irrigation system represents an innovative approach to optimize water usage in agricultural and landscaping practices. The integration of cutting-edge technologies, including sensors, actuators, and data analysis, empowers this system to provide accurate monitoring and control of irrigation processes by leveraging real-time environmental conditions. The main objective of a smart irrigation system is to optimize water efficiency, minimize expenses, and foster the adoption of sustainable water management methods. This paper conducts a systematic risk assessment by exploring the key components/assets and their functionalities in the smart irrigation system. The crucial role of sensors in gathering data on soil moisture, weather patterns, and plant well-being is emphasized in this system. These sensors enable intelligent decision-making in irrigation scheduling and water distribution, leading to enhanced water efficiency and sustainable water management practices. Actuators enable automated control of irrigation devices, ensuring precise and targeted water delivery to plants. Additionally, the paper addresses the potential threat and vulnerabilities associated with smart irrigation systems. It discusses limitations of the system, such as power constraints and computational capabilities, and calculates the potential security risks. The paper suggests possible risk treatment methods for effective secure system operation. In conclusion, the paper emphasizes the significant benefits of implementing smart irrigation systems, including improved water conservation, increased crop yield, and reduced environmental impact. Additionally, based on the security analysis conducted, the paper recommends the implementation of countermeasures and security approaches to address vulnerabilities and ensure the integrity and reliability of the system. By incorporating these measures, smart irrigation technology can revolutionize water management practices in agriculture, promoting sustainability, resource efficiency, and safeguarding against potential security threats.
Harnessing WebAssembly for Real-time Stateless Streaming PipelinesChristina Lin
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DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELgerogepatton
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
TIME DIVISION MULTIPLEXING TECHNIQUE FOR COMMUNICATION SYSTEMHODECEDSIET
Time Division Multiplexing (TDM) is a method of transmitting multiple signals over a single communication channel by dividing the signal into many segments, each having a very short duration of time. These time slots are then allocated to different data streams, allowing multiple signals to share the same transmission medium efficiently. TDM is widely used in telecommunications and data communication systems.
### How TDM Works
1. **Time Slots Allocation**: The core principle of TDM is to assign distinct time slots to each signal. During each time slot, the respective signal is transmitted, and then the process repeats cyclically. For example, if there are four signals to be transmitted, the TDM cycle will divide time into four slots, each assigned to one signal.
2. **Synchronization**: Synchronization is crucial in TDM systems to ensure that the signals are correctly aligned with their respective time slots. Both the transmitter and receiver must be synchronized to avoid any overlap or loss of data. This synchronization is typically maintained by a clock signal that ensures time slots are accurately aligned.
3. **Frame Structure**: TDM data is organized into frames, where each frame consists of a set of time slots. Each frame is repeated at regular intervals, ensuring continuous transmission of data streams. The frame structure helps in managing the data streams and maintaining the synchronization between the transmitter and receiver.
4. **Multiplexer and Demultiplexer**: At the transmitting end, a multiplexer combines multiple input signals into a single composite signal by assigning each signal to a specific time slot. At the receiving end, a demultiplexer separates the composite signal back into individual signals based on their respective time slots.
### Types of TDM
1. **Synchronous TDM**: In synchronous TDM, time slots are pre-assigned to each signal, regardless of whether the signal has data to transmit or not. This can lead to inefficiencies if some time slots remain empty due to the absence of data.
2. **Asynchronous TDM (or Statistical TDM)**: Asynchronous TDM addresses the inefficiencies of synchronous TDM by allocating time slots dynamically based on the presence of data. Time slots are assigned only when there is data to transmit, which optimizes the use of the communication channel.
### Applications of TDM
- **Telecommunications**: TDM is extensively used in telecommunication systems, such as in T1 and E1 lines, where multiple telephone calls are transmitted over a single line by assigning each call to a specific time slot.
- **Digital Audio and Video Broadcasting**: TDM is used in broadcasting systems to transmit multiple audio or video streams over a single channel, ensuring efficient use of bandwidth.
- **Computer Networks**: TDM is used in network protocols and systems to manage the transmission of data from multiple sources over a single network medium.
### Advantages of TDM
- **Efficient Use of Bandwidth**: TDM all
Literature Review Basics and Understanding Reference Management.pptxDr Ramhari Poudyal
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Enhanced convolutional neural network for non-small cell lung cancer classification
1. International Journal of Electrical and Computer Engineering (IJECE)
Vol. 13, No. 1, February 2023, pp. 1024~1038
ISSN: 2088-8708, DOI: 10.11591/ijece.v13i1.pp1024-1038 1024
Journal homepage: http://ijece.iaescore.com
Enhanced convolutional neural network for non-small cell lung
cancer classification
Yahya Tashtoush1
, Rasha Obeidat1
, Abdallah Al-Shorman1
, Omar Darwish2
, Mohammad Al-Ramahi3
,
Dirar Darweesh1
1
Department of Computer Science, Computer and Information Technology, Jordan University of Science and Technology, Irbid, Jordan
2
Information Security and Applied Computing, GameAbove College of Engineering and Technology, Eastern Michigan University,
Ypsilanti, United States
3
CIS, Department of Computing and Cybersecurity, Texas A&M University, San Antonio, United States
Article Info ABSTRACT
Article history:
Received Dec 1, 2021
Revised Jul 14, 2022
Accepted Aug 10, 2022
Lung cancer is a common type of cancer that causes death if not detected
early enough. Doctors use computed tomography (CT) images to diagnose
lung cancer. The accuracy of the diagnosis relies highly on the doctor's
expertise. Recently, clinical decision support systems based on deep learning
valuable recommendations to doctors in their diagnoses. In this paper, we
present several deep learning models to detect non-small cell lung cancer in
CT images and differentiate its main subtypes namely adenocarcinoma,
large cell carcinoma, and squamous cell carcinoma. We adopted standard
convolutional neural networks (CNN), visual geometry group-16 (VGG16),
and VGG19. Besides, we introduce a variant of the CNN that is augmented
with convolutional block attention modules (CBAM). CBAM aims to extract
informative features by combining cross-channel and spatial information.
We also propose variants of VGG16 and VGG19 that utilize a support
vector machine (SVM) at the classification layer instead of SoftMax. We
validated all models in this study through extensive experiments on a CT
lung cancer dataset. Experimental results show that supplementing CNN
with CBAM leads to consistent improvements over vanilla CNN. Results
also show that the VGG variants that use the SVM classifier outperform the
original VGGs by a significant margin.
Keywords:
Computed tomography
Convolutional block attention
module
Convolutional neural networks
Deep learning
Lung cancer
Non-small cell carcinoma
VGG16
This is an open access article under the CC BY-SA license.
Corresponding Author:
Yahya Tashtoush
Department of Computer Science, Jordan University of Science and Technology
P.O. Box 3030, Irbid, 22110 Jordan
Email: yahya-t@just.edu.jo
1. INTRODUCTION
Lung cancer is a fatal disease that contributes to the majority of deaths of cancers throughout the
world [1]. It causes yearly more deaths than the next four leading killer-cancer (colon, breast, prostate, and
pancreas combined) in the US. Smoking and air pollution and exposure to other carcinogens are major risk
factors that encourage the development of lung cancer, especially in low- and middle-income countries [1],
[2]. Hence, taking actions to reduce air pollution is essential to reduce the death rate from lung cancer. In
addition, developing automatic lung cancer diagnosis systems helps greatly in the early detection of the
disease, which significantly improves the cure rates [3].
Lung cancer is the uncontrolled growth of abnormal cells in lung tissues. It is categorized into two
main types: small cell lung carcinoma (SCLC) and non-small-cell lung carcinoma (NSCLC). NSCLC is more
common accounting for 85% of all cases [4]. The type of lung cancer has a staging system that doctors use to
describe how advanced the cancer is and to develop the best possible treatment plan accordingly. A four-
2. Int J Elec & Comp Eng ISSN: 2088-8708
Enhanced convolutional neural network for non-small cell lung cancer classification (Yahya Tashtoush)
1025
stage system is used to describe the NSCLC severity. Stage 1 means that cancer is confined to the lung and
stage 2 is assigned when cancer has reached nearby lymph nodes. In stage 3, cancer has spread to other lymph
nodes in the chest, while stage 4 means that cancer has spread from the chest to other parts of the body [5]. In
the late stages, surgical treatment might no longer be possible [6]. Radiation, chemotherapy, and
immunotherapies are among the alternative less-effective nonsurgical options that tumors in late stages are
treated with [7].
NSCLC has several subtypes that can occur in variant histologic shapes. The most common
subtypes are adenocarcinoma, squamous cell carcinoma, and large cell carcinoma, representing 50%, 35%,
and 10% of NSCLC cases respectively [8], [9]. Figure 1 presents computed tomography (CT) scan samples
of a normal lung, as shown in Figure 1(a), and lungs infected with one of the main three subtypes of NSCLC.
Despite sharing many biological features, NSCLC subtypes vary in the location within the lung, the type of
infected cells, and the growth patterns [10]. For instance, Adenocarcinomas mostly arise in peripheral areas
of the lung [1], as shown in Figure 1(b), while squamous cell carcinoma tends to grow near the center of the
lung [10] as shown in Figure 1(c). Large cell carcinoma (also known as undifferentiated carcinomas) tends to
develop in any part of the lung and spreads to the lymph nodes and distant sites [11], as shown in Figure 1(d).
(a) (b) (c) (d)
Figure 1. Examples of CT scans of normal lung vs. NSCLC infected lungs: (a) normal lung,
(b) adenocarcinoma, (c) large cell carcinoma, and (d) squamous cell carcinoma
It is crucial to provide an accurate distinction between the NSCLC subtypes for deciding on the
optimum therapeutic modality [12]. Different NSCLC subtypes exhibit varying sensitivity and resistance to
different chemotherapies [1]. For example, Pemetrexed (a multiple-enzyme inhibitor) is substantially more
effective in the treatment methods of Adenocarcinoma patients compared to squamous cell carcinoma
patients [13]. Moreover, deciding the treatment protocol for the miss-identified NSCLC subtype could even
be life-threatening. For instance, patients with adenocarcinoma have a good response to Bevacizumab (a type
of medication). In contrast, patients with squamous cell carcinoma could suffer from life-threatening
hemoptysis (i.e., coughing up of blood) when treated with the same type of medication [14].
However, it is challenging and time-consuming for radiologists and pathologists to give a proper
diagnosis. This is due to the high heterogeneity of tumor tissues [15] and subtle morphological differences
between NSCLC subtypes [16]. The visual assessment of CT images of lung tissues is one of the main
methods used by pathologists to determine the stage and subtypes of lung cancer. However, the manual
assessment of CT scans has been shown to be subjective and inaccurate in certain diagnosis cases of tumor
subtypes and stages [15]. A recent study shows that the overall agreement for classifying adenocarcinoma
and squamous cell certified by peer review is unsatisfactory even among expert lung pathologists [17].
Furthermore, the number of pathologists qualified to perform a visual-only assessment of CT images may not
be enough to meet the rising demands.
In recent years, deep neural networks (DNNs), especially convolutional neural networks (CNNs),
have become the dominant method in classifying and discriminating lung nodules from medical images (e.g.,
CT scans). These models are capable of capturing complex patterns and recognizing the subtle differences
between the subtypes of lung cancer and predicting the stage with an impressive speed and accuracy.
Combined with regular screening of at-risk populations (e.g., smokers), deep learning-based diagnosis
systems techniques can substantially improve the disease’s early-stage detection, thus increasing the survival
rates.
In this paper, we investigate the effectiveness of several deep neural models, including basic CNN,
visual geometry group-16 (VGG-16), and VGG19 in detecting NSCLC and identifying its main histological
subclass. CNN, VGG16, and VGG19 are neural architectures composed of feature extraction layers and a
classification layer. The Feature extraction layer stacks several convolutional layers, pooling layer, and
nonlinearity on top of each other to obtain a deep architecture capable of obtaining a strong representation of
3. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 13, No. 1, February 2023: 1024-1038
1026
the lung CT scan. These representations capture subtle differences between the CT scans of different cancer
types that cannot be noticed easily by the naked eye. The classification layer typically implements a SoftMax
function to produce a probability distribution over the four classes (i.e., normal lung, adenocarcinoma, large
cell carcinoma, and squamous cell carcinoma). VGG 16 and VGG19 use deeper neural architecture than
CNN, thus generating better representations. We also propose variants of VGG16 and VGG19 that employ
support vector machine (SVM) [18] to perform classification as an alternative to the SoftMax function.
Moreover, we empower CNN by combining it with convolutional attention block modules [19]. The attention
mechanism added enabled the CNN algorithm to focus on the most important informative special part of the
CT images. Thus, enhancing its capability to extract more powerful representation from the images, hence
improving the classification performance. The contributions of this paper are:
− Implementing and comparing three deep learning algorithms, namely CNN, VGG16 and VGG19 to
classify lung CT images under one out of four classes: normal lung (no cancer), adenocarcinoma,
squamous cell carcinoma, and large cell carcinoma,
− Developing modified versions of VGG16 and VGG19 that use SVM for classification instead of the
typical SoftMax function,
− Enhancing the basic CNN architecture by incorporating convolutional attention block modules to help the
CNN to focusing on the important regions of the lung CT scan, which is critical to obtain a correct
classification
− Presenting experimental results that demonstrate the effectiveness of the deep learning approaches
explored in this study in identifying NSCLC subtypes. All models are trained on a multi-class NSCLC
dataset publicly available on Kaggle. In addition, we proved experimentally the consistent advantage that
SVM adds to VGGs. We also showed the superior performance of attention-enhanced CNN in
comparison with the basic CNN.
In the last decade, several studies have shown the effectiveness of machine learning and deep
learning techniques in providing an accurate diagnosis and prognosis of various cancer types such as gastric
and colorectal cancer [20], [21], blood cancer [22], prostate cancer [23], [24], breast cancer [25], [26], and
lung cancer [14], [16], [27].
Ahmed and Kashmola [28] provided an approach for the classification of malicious skin diseases.
They made a comparison between the precision of their prediction since two pernicious diseases of skin
diseases were predicted which are basal cell carcinoma and melanoma disconnectedly with photos of nevus.
They proposed architecture for processing in deep learning. It depends on the convolutional neural network
technology which relies on a set of layers. Bedeir et al. [29] applied both dermatologists and current deep
learning techniques for the classification of skin cancer. The achievement of two pre-trained CNNs was
assisted, and the integration of their results suggested the best method for skin cancer classification on the
HAM10000 dataset with a precision 94.14%.
Other research showed the efficiency of data mining for classification in other diseases. Panda et al.
[30] introduced in their paper an efficient technique with high accuracy to detect diabetes disease. The
authors utilized the K-nearest neighbor method to decrease the processing time. On the other hand, they used
also support vector machine to assign its particular class for every sample of data. They also utilized the
feature selection method to build their machine learning model. Overall, the authors used four techniques to
assist which can with ease detect whether or not a person will endure from diabetes. Nugroho et al. [31]
focused on efficient modeling that is able to detect coronary artery disease (CAD) by utilizing feature
selection attribute for dealing with high dimensional data and feature resampling to deal with unbalanced
data. Hyperparameter tuning to locate the best integration of parameters in SVM is also performed.
Regarding lung cancer particularly, deep learning algorithms, especially CNN and its variants, have
shown exceptionally good performance in detecting lung cancer [32], identifying its subtypes [15], [16], [27],
stages [33], providing treatment assessment [7], [34], and perform lung field segmentation [35]. With the
availability of digital histopathology images, and the unprecedented advances in the computational resources,
deep neural models can be trained on millions of histopathology images, and effectively capture the
distinctive histopathology patterns of cancer cells.
Khosravi et al. [15] demonstrate the power of deep learning approaches for identifying NSCLC
subtypes. They experimented with basic CNN architecture, Google’s Inceptions, and an ensemble of
Inception and Reset to distinguish adenocarcinoma from squamous cell carcinoma. Moitra and Mandal [17]
utilized CNN to identify non-small cell lung tumor regions from adjacent dense benign tissues and to identify
the major subtypes of both adenocarcinoma and squamous cell carcinoma. Another work that is proposed by
Guo et al. [36] presents and assesses the performance of two novel CNN-based architectures trained on a
dataset of CT images to differentiate between lung adenocarcinoma, squamous cell carcinoma, and small cell
lung cancers. Han et al. [37] investigated the efficacy of various deep learning techniques and machine
4. Int J Elec & Comp Eng ISSN: 2088-8708
Enhanced convolutional neural network for non-small cell lung cancer classification (Yahya Tashtoush)
1027
learning algorithms and combined with several feature selection methods in differentiating the histological
subtypes of NSCLC.
The recent availability of digital histopathology whole-slide images (WSIs) leads to the
development of neural models that can be learned from high-resolution WSIs. Dealing with WSIs directly is
considered challenging due to their high spatial resolution. Usually, they are divided into multiple patches
and receive detailed annotations before being fed to models. Chen et al. [16] propose a slide-level diagnosis
model to detect adenocarcinoma and squamous cell carcinoma trained on WSIs. The proposed model
incorporates the unified memory (UM) mechanism and several GPU memory optimization techniques to
train CNNs on WSIs using slide-level labels, which alleviate the need for laborious multiple-patch annotation
by pathologists.
A similar study is carried out by Coudray et al. [27] who trained inceptionv3, a deep CNN-based
deep model, on histopathology to classify whole-slide pathology images of lung tissues into normal,
adenocarcinoma, or squamous cell carcinoma. Kanavati et al. [38] built a CNN model based on the
EfficientNet-B3 architecture, using transfer learning and weakly-supervised learning, to predict carcinoma in
whole slide imaging (WSIs) using partially labeled training data. Another pertaining line of research is
tracking the changes that happen to lung cancer patients after receiving treatment. Xu et al. [7] use a deep
learning model, namely CNN and RNN (recurrent neural networks), to predict survival and other clinical
outcomes. Time-series CT images of patients are analyzed with locally advanced NSCLC by incorporating
pretreatment and CT images are follow-up. Chaunzwa et al. [34] proposed a radionics approach to predicting
NSCLC tumor histology using trained and convolutional neural networks (CNNs). They used a CT scan
dataset of early-stage NSCLC patients who received surgical treatment at Massachusetts General Hospital
(MGH). The study focuses on the two most common NSCLC types: adenocarcinoma (ADC) and squamous
cell carcinoma (SCC). They compared CNN against two machine learning approaches (SVM and k-nearest
neighbors) and found that both showed a comparable performance.
In another related line of research and as a pretreatment step in the diagnosis of lung diseases,
authors proposed methods for pathological lung segmentation (e.g., [39]–[41]). Wang et al. [40] introduced a
data-driven model, called the central focused convolutional neural networks (CF-CNN), to segment lung
nodules from heterogeneous CT images. In the same line, Aresta et al. [41] proposed iW-Net, a deep learning
model that allows for both automatic and interactive segmentation of lung nodules in computed tomography
images. To the best of our knowledge, there is no other research article reported results on this dataset before.
The summary of related work is presented in Table 1.
Table 1. Deep learning models for lung cancer classification task
Study Dataset Size Classes Model Accuracy
Khosravi
et al. [15]
The Stanford Tissue
Microarray Database
(TMAD) and The Cancer
Genome Atlas (TCGA)
12,139
images
ADC &
SCC
CNN inceptions, and
an ensemble of two
models inception and
ResNet
Achieve an accuracy of 100%, 92%,
95% and 69% for various cancer
tissues, subtypes, biomarkers, and
scores, respectively
Moitra and
Mandal [17]
PET/CT scans. From
(TCIA)
211
patients
NSCLC. Proposed a 1D CNN
to detect NSCLC.
Achieve an accuracy 96%
Guo et al.
[36]
CT Images 1,016
patients
ADC,
SCC, and
SCLC.
The ProNet and
com_radNet models
Achieve an accuracy of 71.6% and
74.7% for ProNet and com_radNet
models, respectively.
Han et al.
[37]
PET/CT images 867
patients
ADC &
SCC.
VGG 16 Achieve an accuracy of 84.10%
Chen et al.
[16]
WSIs images 9,662
images
ADC &
SCC.
Purpose slide-level
diagnoses model
Achieve an accuracy of 95.94%
and 94.14% for ADC and SCC
Coudray
et al. [27]
WSIs images 1,634
images
ADC &
SCC.
Inception v3 Achieve an accuracy of 97.00%
Kanavati
et al. [38]
WSIs images 3,554
images
NSCLC. (CNN) based on the
EfcientNet-B3
architecture
Achieve an accuracy on four
independent est sets of 0.975,
0.974, 0.988, and 0.981,
respectively
Xu et al. [7] CT images 179
patients
NSCLC. CNN with RNN Achieve an accuracy of 74.00%
Chaunzwa
et al. [34]
CT images 179
patients
ADC &
SCC
CNN. Achieve an accuracy of 71.00%
2. MATERIALS AND METHODS
Deep learning techniques have become one of the most promising and widely used in medical
images analysis. Convolutional neural networks (CNNs) are the most popular and regularly used deep
learning algorithms [42]. They represent a huge breakthrough in image classification. In general, CNN-based
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architectures are composed of an input layer that reads the image, hidden layers that work together for feature
extraction, and a classification layer that outputs the predicted class. The hidden layers are stacked blocks of
convolutional layers, pooling, and nonlinear activation functions. Convolutional layers apply a convolution
operation to the input images and pass the resulting feature maps to the next layer. The non-linear activation
functions (e.g., rectified linear unit (ReLU)) are applied to add non-linearity to the network and increase its
ability to learn very complex structures from the images. Pooling layers reduce representation size and allow
the network to achieve spatial variance. There are two pooling methods commonly applied: max-pooling
(MP) and average pooling (AP). This section describes the main CNN-based architecture used in this work,
including basic CNN, VGG16, and VGG19. Also, we describe the architecture modification we have added
to the CNN and the VGG models to increase their predictability. We additionally describe the dataset used in
this work and explain the evaluation metric and the training process of our deep models. The methodology of
our research project is explained in Figure 2.
Figure 2. The research project methodology
2.1. CNN model
The architecture of the CNN model we use for NSCLC subtype classification is shown in Figure 3.
It shows our CNN 5 convolutional layers and 3 max-pooling layers placed after the third, the fourth, and the
fifth convolutions. We apply ReLU non-linear activation function after each convolution operation. All the
convolutional layers use filters with size 3×3. The first convolutional layer uses 256 filters with size 3.
Each of the following convolutions uses half of the filter count of the previous layer with the same filter size
(i.e., the numbers of filters are: 256, 128, 64, 32, 16). The max-pooling size is 2×2. To perform classification,
we use three Fully Connected (FC) layers that are followed by the SoftMax function in the output layer. The
SoftMax function gives a 4D (four-dimension) vector representing the probability distribution over the four
classes used in this study. The most probable class (class with the highest probability value) is selected. We
place a flattened layer right before the first fully connected layer to convert the 3D feature map extracted by
the last convolution layer to 1D being fed to the first fully connected layer.
Figure 3. The architecture of the CNN model
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2.2. CNN with attention (CNN+CBAM)
This section describes our approach to enhance our basic CNN by being combined with
convolutional block attention module (CBAM). CBAM was originally proposed by Woo et al. [19]. The idea
of augmenting CNN with attention mechanism is not new; it has previously been applied to a variety of
natural language processing and computer vision tasks such as sentence pair modeling [43], relation
extraction [44], sentence classification [45], and image classification [46]. The main intuition is that CBAM
allows CNN to emphasize the informative regions in the CT images and filter out the meaningless
background regions that do not differentiate different classes. The framework of our augmented CNN model
(CNN+CBAM) is the same as the CNN model, with three CBAM blocks inserted after the third, fourth, and
fifth convolutional layers. The architecture of CNN+CBAM model is presented in Figure 4.
Figure 4. The architecture of the CNN+CBAM model
In the following, we describe the convolutional block attention. We use the same notations as the
original work [19]. CBAM aims to emphasize the informative feature and suppress the non-useful
information by using channel and spatial attention modules as illustrated in Figure 5. Whereas the channel
module focuses on what to attend, the spatial module concentrates on where to attend. Our intuition behind
using CBAM is that the channel attention module could assist in differentiating abnormal spots in lung from
normal tissues, while the spatial attention helps to focus on the location of the tumor, which is essential in
deciding the cancer if any as different types tend to infect different parts of the lung.
Figure 5. The structure of convolutional block attention module (CBAM) [19]
Each CBAM block takes the feature map from the previous convolution layer as input and output of
a refined feature map that is passed to the next layer. Let F ∈ RC×H×W
be the input feature map to CNBM
block, where C,H and W are the number of the channels, the width, and the height of F respectively. F is
passed first to the channel attention module, which output is Mc ∈ R1×H×W
, a one-dimensional channel
attention map. The first refined feature map,
𝐹′
= 𝑀𝑐(𝐹) ⊕ 𝐹 (1)
where ⊕ denotes element-wise multiplication. F′
is assumed to summarize the important information of the
image. After that, F′
is passed to the spatial attention module and outputs the final refined feature F′′
that is
computed as (2).
𝐹′′
= 𝑀𝑐(𝐹′) ⊕ 𝐹′
(2)
F′′
(the output of the CBAM block) is assumed to capture the important information located within the
image. To learn about the details of the channel and spatial attention modules, please refer to [19].
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2.3. VGG16 and VGG19
VGGNet [47] was originally invented by visual geometry group (VGG) from the University of
Oxford. It was the runner-up of the localization and classification tracks of the ImageNet large scale visual
recognition challenge (ILSVRC-2014) competition. VGGNet reveals that increasing the depth of the network
while using small convolutional filters (e.g., 3×3) contributes to learning more complex representations
without increasing the number of parameters to be learned. VGG16 and VGG19 are two versions of VGGNet
that were more successful in the ILSVRC competition than the others. They have gained wide popularity and
applied to a variety of tasks [48].
VGG16 consists of 13 convolutional layers and three dense fully connected layers with 4,096;
4,096; and 1,000 neurons followed by a SoftMax layer on top to perform classification. VGG19 differs from
VGG16 in that it has 16 convolutional layers instead of 13. Both networks use a stack of small convolutional
filters of size 3×3 with stride 1, followed by multiple max-pooling and ReLU activation function to achieve
non-linearity as shown in Figure 6. We have chosen VGGNets in this study as it has proven to work well in
practice with small-size image datasets [49].
Figure 6. VGG16 and VGG19 model architectures
2.4. Modified VGG 16 and VGG19
Classical VGG models employ the SoftMax activation function for prediction. In addition, the
training is performed by minimizing cross-entropy loss function. Tang [50] demonstrated that replacing the
SoftMax layer with a linear multi-class support vector machine yields a small yet consistent advantage to the
predictability of convolutional neural network. This is in concordance with what mentioned in study [48].
Inspired by that, we propose modified versions of VGG16 and VGG19 using SVM for classification
instead of SoftMax. We refer to the modified VGG nets as VGG16-SVM and VGG19-SVM. As presented in
Figure 7, the modified VGGNets replace the three fully connected (dense) layers in the original models with
only two dense layers with 256 and 128 neurons. Besides, learning is carried out by minimizing a
margin-based loss instead of the cross-entropy loss. The 3D feature map extracted by the last convolution
layer is mapped to 1D vector by going through a flattened layer before being fed to the first fully connected
layer.
Figure 7. VGG16-SVM and VGG19-SVM model architectures
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2.5. Dataset
The dataset we use in this work is freely available Kaggle dataset. It has been developed and
released to encourage data scientists and medical informatics communities to develop effective lung cancer
detection algorithms. The dataset consists of 1000 CT slicers from different cases divided between CT scans
of normal lungs and NSCLC-infected lungs. Each CT scan is a whole-slide image of size 256×256 annotated
with one of the classes: normal, adenocarcinoma, squamous cell carcinoma, or large cell carcinoma. The
scans either have JPG or PNG format (low resolution one-slide images).
The dataset is divided into training/testing/validation sets. We use the training set to fit the models
while the testing set is used to evaluate the performance of the final models. The validation set is used to
choose between the possible design options and provide an unbiased evaluation of a model trained on the
training dataset while tuning model hyperparameters. The class distribution of the dataset is presented in
Table 2. To the best of our knowledge, there is no other research article that reported results on this dataset
before.
Table 2. Class distribution of lung cancer dataset including the number of
CT scans of each of the four in the training, testing, and validation sets
File Class Size Total size
Training Normal
Adenocarcinoma
Large cell carcinoma
Squamous cell carcinoma
148 images
195 images
115 images
155 images
613 images
Validation Normal
Adenocarcinoma
Large cell carcinoma
Squamous cell carcinoma
13 images
23 images
21 images
15 images
72 images
Testing Normal
Adenocarcinoma
Large cell carcinoma
Squamous cell carcinoma
54 images
120 images
51 images
90 images
315 images
2.6. Training and evaluation
To select the most appropriate architectures for a given task and classification aim, we carried out
several experiments for each model by varying the choices of hyper-parameters. For every model we trained,
we tuned all the hyper-parameters using the validation set before the final evaluation on the testing set.
Hyperparameter optimization was explored iteratively. The hyper-parameters we tuned are the learning rate,
the batch size, the optimizer, the number of epochs, and the use of dropout (by trying several values ranges
from 0.1 to 0.5) vs not using it. The predictive performance of the models was evaluated with accuracy (A),
which is the percentage of images that have been classified correctly by a model.
𝐴 =
𝑇ℎ𝑒 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑖𝑚𝑎𝑔𝑒𝑠 𝑐𝑙𝑎𝑠𝑠𝑖𝑓𝑖𝑒𝑑 𝑠𝑢𝑐𝑐𝑒𝑓𝑓𝑢𝑙𝑙𝑦
𝑇𝑜𝑡𝑎𝑙 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑖𝑚𝑎𝑔𝑒𝑠
(3)
Inputs to the CNN models are 64×64-pixel image patch and it is 64×64 for all the VGG models. We
use Adam optimizer [51] with learning rate set to 0.001 for CNN and CNN+CBAM and 0.0001 for VGGNets
(VGG16, VGG19, VGG16-SVM, and VGG19-SVM). All models are trained for 25 epochs with early
stopping. We noticed performance degradation on the validation set after epoch 25 the batch size was set to
32. Before being fed to CNN and CNN+CBAM, all images are scaled to 64-by-64 pixels. The image sizes for
VGG16, VGG19, and their variants are selected as pixels. Tables 3 to 5 show the best hyperparameter of our
deep models that performed the classification outcomes.
Table 3. Hyperparameters for CNN and CNN with attention model
Hyperparameters Value
Learning rate 0.001
Batch size 32
Loss function Categorical cross entropy
Activation function ReLU
Optimizer Adam
Number of epochs 15
Steps per epoch 20
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Table 4. Hyperparameters for VGG16 and VGG19 model
Hyperparameters Value
Learning rate
Batch size
Loss function
Activation function
Optimizer
Number of epochs
Steps per epoch
Classifier
1.0e-4 (VGG16)
1.0e-3 (VGG19)
32
Categorical cross entropy
ReLU
Adam
25
20
Soft-Max
Table 5. Hyperparameters for modified VGG16 and VGG19 model
Hyperparameters Value
Learning rate
Batch size
Loss function
Activation function
Optimizer
Number of epochs
Steps per epoch
Classifier
1.0e-4 (VGG16)
1.0e-3 (VGG19)
32
Categorical cross entropy
ReLU
Adam
25
20
SVM
3. RESULTS AND DISCUSSION
In this section, we present the experimental results of the CNN-based neural architectures utilized
in this study. Figure 8 shows the testing accuracy of basic CNN, CNN+CBAM, VGG16, VGG19,
VGG16+SVM, VGG19+SVM. It can be easily noticed that VGG16+SVM is able to detect NSCLC and its
subtypes with highly reliable accuracy (83.4%), and it outperforms all other models by a significant margin.
Table 6 shows the result of precision, recall, F1-score, support, macro averaging (AVG), weighted AVG, and
accuracy for each model of classes adenocarcinoma, large cell carcinoma, normal and squamous cell
carcinoma. VGG16 with SVM can efficiently detect NSCLC and its subtypes with highly reliable accuracy
(83.49%). It outperforms all other models by precision, recall, F1-score, support, macro AVG, and weighted
AVG, as shown in Table 6. Notably, attention mothed outperforms the CNN model in macro AVG and
weighted AVG of (precision, recall, F1-score) by 70.16%, 72.09%, and 70.59% in macro AVG and 74.49%,
72.38%, and 72.96% in weighted AVG, respectively. Also, improve the accuracy by 7.3%.
Figure 8. Accuracy of CNN, CNN+CBAM, VGG16, VGG19, VGG16+SVM and VGG19+SVM models
evaluated on the testing set
Recently, there has been some discussion about whether data is the new oil or not. The training
images are the variation typically found within the class. If the images in a category are similar, fewer images
might be acceptable. Usually, about 100 images are good to train a class. NSCL cancer is a leading cause of
cancer mortality worldwide. The early detection and the accurate diagnosis of NSCL are essential to improve
survival rates and to set customized treatment protocols. Our study used a small dataset to classify lung
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cancer, and we got a good result, as shown in Figure 8. We encourage studies to work on the small dataset,
and we agree that the data is the new oil. For example, when we talk about classifying small cell lung cancer,
it constitutes a small percentage of all cases, but it has a high risk. The advantages that SVM brings to
VGGNets and CBAM brings to CNN suggest that combining CBAM and VGGNets for feature extraction
with using SVM for classification could lead to further performance, which we will leave for future work.
Table 6. Precision, recall, F1-score, support, macro AVG, weighted AVG and accuracy for each model of
classes’ adenocarcinoma, large cell carcinoma, normal and squamous cell carcinoma
Model name Class Precision % Recall % F1-score % Support Accuracy %
CNN Adenocarcinoma 85.11 % 66.67 % 74.77 % 120 65.08%
Large cell carcinoma 46.38 % 62.75 % 53.33 % 51
Normal 47.95 % 64.44% 55.12% 54
Squamous cell carcinoma 73.42% 64.44% 68.64% 90
Macro AVG % 63.21% 64.67% 62.96% 315
Weighted AVG% 69.13% 65.08% 66.18% 315
CNN with attention Adenocarcinoma 85.44% 73.33% 78.92% 120 72.38%
Large cell carcinoma 61.02% 70.59% 65.45% 51
Normal 54.93% 72.22% 62.40% 54
Squamous cell carcinoma 79.27% 72.22% 75.58% 90
Macro AVG % 70.16% 72.09% 70.59% 315
Weighted AVG% 74.49% 72.38% 72.96% 315
VGG16 Adenocarcinoma 86.24% 78.33% 82.10% 120 78.08%
Large cell carcinoma 68.42% 76.47% 72.22% 51
Normal 72.88% 79.63% 76.11% 54
Squamous cell carcinoma 77.78% 77.78% 77.78% 90
Macro AVG % 76.33% 78.05% 77.05% 315
Weighted AVG% 0.7865 0.7810 0.7824 315
VGG16 with SVM Adenocarcinoma 89.29% 83.33% 86.21% 120 83.49%
Large cell carcinoma 84.00% 82.35% 83.17% 51
Normal 68.66% 85.19% 76.03% 54
Squamous cell carcinoma 87.21% 83.33% 85.23% 90
Macro AVG % 82.29% 83.55% 82.66% 315
Weighted AVG% 84.30% 83.49% 83.69% 315
VGG19 Adenocarcinoma 83.18% 74.17% 78.41% 120 74.60%
Large cell carcinoma 66.67% 74.51% 70.37% 51
Normal 65.62% 77.78% 71.19% 54
Squamous cell carcinoma 75.86% 73.33% 74.58% 90
Macro AVG % 72.83% 74.95% 73.64% 315
Weighted AVG% 75.41% 74.60% 74.78% 315
VGG19 with SVM Adenocarcinoma 91.35% 79.17% 84.82% 120 79.05%
Large cell carcinoma 67.80% 78.43% 72.73% 51
Normal 68.75% 81.48% 74.58% 54
Squamous cell carcinoma 79.55% 77.78% 78.65% 90
Macro AVG % 76.86% 79.21% 77.69% 315
Weighted AVG% 80.29% 79.05% 79.34% 315
3.1. CNN+CBAM vs CNN
The superior performance of CNN+CBAM (72.38) over CNN (65.08) as shown in Figure 7
demonstrates the ability of CNN+CBAM to learn a higher level of structural patterns typical to subclasses of
NSCLC. We believe that the channel attention modules help in differentiating abnormal cancer cells from the
normal cells, which is important to distinguish the normal lung from the cancer-infected lung. On the other
hand, the spatial attention module assists in capturing the location of the tumor, which is crucial in
differentiating the subtypes that usually affect varying locations within the lung. For instance,
adenocarcinoma tends to grow in peripheral areas of the lung. On the contrary, squamous cell carcinoma
tends to appear in the central airways of the lung. Table 7 reports the per-subtype accuracy achieved by CNN
and CNN-CBAM. The results show a consistent gain of incorporating CBAM for all classes.
Table 7. Accuracy for every class adenocarcinoma, large cell carcinoma,
normal and squamous cell carcinoma, with CNN and CNN-CBAM
Classes CNN CNN-CBAM
Adenocarcinoma 66.66 73.33
Large cell carcinoma 62.74 70.58
Normal
Squamous cell carcinoma
64.81
64.44
72.22
72.22
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3.2. VGGNets vs the modified VGGNets
As we can see in Figure 8, the results suggest that replacing the SoftMax function with SVM
increases the prediction accuracy. We believe the performance gain is largely due to SVMs that can draw the
optimal hyperplane separating different classes in the dataset [18]. It exhibits the greatest possible distance
(maximum margin) between the closest data points across different classes. Once the relevant support vectors
(the closest data points to the margin) are contained in the training set, the optimization will always result in
the same classifier, even with limited training data that is considerably small [52]. Thus, SVMs are much
more robust to learn generalizable models from the small amount of training data as the one we use in this
study. Opposing our expectations, VGG16+SVM gives better performance than VGG19+SVM by 3.5% as
shown in Figure 8, even though VGG19 is deeper than VGG16 and is supposed to extract a stronger
representation of the CT scans. This might be due to the relatively small-size training set used in this work,
which could make more complex architecture suffer from overfitting as more parameters are needed to be
learned.
Figures 9(a) and (b) plot the training and the validation accuracy and loss of VGG16 and VGG16-
SVM against epoch numbers. The curve reveals the consistent advantage of incorporating SVM for
classification starting from early epochs (starting from epoch 4). This in turn means that the modified
VGG16-SVM model can achieve comparable performance to VGG16 in shorter training time. It is also
remarkable to notice that the validation accuracy keeps improving slightly even after the training accuracy is
stabilized (at epoch 5). We think that is because the quality of the margin that SVM learns keeps improving
even after the best possible training accuracy is achieved. However, we can see that the validation accuracy
and loss curves of VGG16-SVM are more fluctuating than the loss of VGG16, which means that the number
of epochs needs to be tuned more carefully with VGG16-SVM to get the best possible models.
(a)
(b)
Figure 9. Per-epoch training and validation accuracy and loss of VGG16 and modified VGG16-SVM in
(a) VGG16 model and (b) VGG16-SVM model
The experiment on the two models was 15 epochs. We recognize that the modified VGG16 with the
SVM model gets an accuracy of 93.94%, while the VGG16 model gets 76.88% for epoch number 3, as shown
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in Figure 9(b). The modified VGG16 with the SVM model is better than the VGG16 model in accuracy and
loss. In terms of the gap between the training and validation of the two models, the modified VGG16 with the
SVM model gets an accuracy of 99.94%, while the VGG16 model gets 98.91% in the last epoch.
4. CONCLUSION
Non-small cell lung cancer is a leading cause of cancer mortality worldwide. The early detection
and the accurate diagnosis of NSCL are essential to improve survival rates and to set customized treatment
protocols. In this study, we build several deep learning models, including basic five-layers CNN, VGG16,
and VGG19 to differentiate normal lungs from lungs infected by one of the following subtypes of NSCLC:
adenocarcinoma, squamous cell carcinoma, and large cell carcinoma. In addition, we propose an extension to
CNN that involves CBAM attention blocks to enable the network to focus on the informative part of the
image to obtain a correct classification. Furthermore, we propose VGG16-SVM and VGG19-SVM variants
of VGG16 and VGG19 models that employ SVM in the classification layer instead of the SoftMax function.
They are trained by optimizing max-margin objective function instead of categorical cross-entropy.
Results demonstrated the utility of CNN and VGGNets in detecting and classifying NSCLC. The
results show also that the attention-augmented CNN (CNN+CBAM) outperforms the basic CNN by 7.3%.
Besides, the experiments also manifest the consistent advantage the SVM brings to VGGNets in comparison
to the SoftMax function. The modified VGG16 with SVM outperforms all models by 83.49%. Despite the
small size of the dataset, we got a good result by using the attention method and the enhancement of
pre-training models on VGGNet by using SVM instead of SoftMax. The attention method and SVM as a
classifier give us more features extracted and significantly more learning parameters to learn.
In the future, we will try to collect data of a suitable size to get better results by training the model
on many different images to increase the learning rate and the ability to identify the type of lung cancer
accurately. Also, we will attempt to build a model that outperforms the well-known deep learning models,
and we will try to use the attention method to get better results. Moreover, we will try new techniques to
show better results, such as ensemble or generative adversarial networks (GANs).
ACKNOWLEDGEMENTS
The authors would like to acknowledge the support from the Jordan University of Science and
Technology who funded this research (Faculty Member Research Grant (FMRG)/No. 666 – 2021).
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BIOGRAPHIES OF AUTHORS
Yahya Tashtoush is an associate professor at the College of Computer and
Information Technology, Jordan University of Science and Technology, Irbid, Jordan. He
received his B.Sc. and M.Sc. degrees in Electrical Engineering from Jordan University of
Science and Technology, Irbid, Jordan in 1995 and 1999. He received his Ph.D. degree in
Computer Engineering from the University of Alabama in Huntsville and the University of
Alabama at Birmingham, AL, USA in 2006. His current research interests are software
engineering, cybersecurity, artificial intelligence, robotics, and wireless sensor networks. He
can be contacted at yahya-t@just.edu.jo.
Rasha Obeidat is an assistant professor at the College of Computer and
Information Technology, Jordan University of Science and Technology, Irbid, Jordan. She
received her B.Sc. and M.Sc. degrees in Computer Science from Jordan University of Science
and Technology, Irbid, Jordan in 2005 and 2009. She received her Ph.D. degree in Computer
Science from Oregon State University, USA in 2019. Her current research interests are natural
language processing, deep learning, and machine learning. She can be contacted at email:
rmobeidat@just.edu.jo.
Abdallah Al-Shorman is an active researcher at Jordan University of Science and
Technology, Irbid, Jordan. His current research interests are deep learning, IoT, data mining,
and machine learning. He can be contacted at amalsharman19@cit.just.edu.jo.
Omar Darwish received the M.S. degree from the Jordan University of Science
and Technology, Jordan, and the Ph.D. degree in computer science from Western Michigan
University, USA. He has worked as a visiting assistant professor with the West Virginia
University Institute of Technology, a software engineer with MathWorks, and a programmer
with the Nuqul Group. He is currently an assistant professor with the Department of
Information Security and Applied Computing, Eastern Michigan University. His research
interests include cybersecurity, IoT, and machine learning. He can be contacted at email:
odarwish@emich.edu.
15. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 13, No. 1, February 2023: 1024-1038
1038
Mohammad Al-Ramahi is an assistant professor of Computing Information
Systems (CIS) at Texas A&M-San Antonio/Department of Computing and Cybersecurity. His
research involves data analytics and text mining. He has published in refereed journals like
ACM Transactions on Management Information Systems (TMIS), and ACM Transactions on
Social Computing (TSC). He can be contacted at Mohammad.Abdel@tamusa.edu.
Dirar Darweesh received the M.S. degree in computer science from Jordan
University of Science and Technology, Jordan. He has worked as a lecturer with Jordan
University of Science and Technology. His research interests include cybersecurity, IoT and
data mining. He can be contacted at dmdirar12@cit.just.edu.jo.