Deep convolutional neural networks (CNNs) represent one of the state-of-the-art methods for image classification in a variety of fields. Because the number of training dataset images in biomedical image classification is limited, transfer learning with CNNs is frequently applied. Breast cancer is one of most common types of cancer that causes death in women. Early detection and treatment of breast cancer are vital for improving survival rates. In this paper, we propose a deep neural network framework based on the transfer learning concept for detecting and classifying breast cancer histopathology images. In the proposed framework, we extract features from images using three pre-trained CNN architectures: VGG-16, ResNet50, and Inception-v3, and concatenate their extracted features, and then feed them into a fully connected (FC) layer to classify benign and malignant tumor cells in the histopathology images of the breast cancer. In comparison to the other CNN architectures that use a single CNN and many conventional classification methods, the proposed framework outperformed all other deep learning architectures and achieved an average accuracy of 98.76%.
The document describes using a convolutional neural network with the VGG16 architecture to classify lung cancer CT scan images into 4 classes: large cell carcinoma, squamous cell carcinoma, adenocarcinoma, and normal lungs. The model is trained on a dataset of 1000 CT scan images from Kaggle and achieves an AUC of 0.94, indicating high accuracy in identifying different types of lung cancer. This CNN model with pre-trained VGG16 weights provides an effective approach for classifying lung cancer images and could help enable early diagnosis and treatment of lung cancer.
An intelligent mammogram diagnosis system can be very helpful for radiologist in detecting the abnormalities earlier than typical screening techniques. This paper investigates a new classification approach for detection of breast abnormalities in digital mammograms using League Championship Algorithm Optimized Ensembled Fully Complex valued Relaxation Network (LCA-FCRN). The proposed algorithm is based on extracting curvelet fractal texture features from the mammograms and classifying the suspicious regions by applying a pattern classifier. The whole system includes steps for pre-processing, feature extraction, feature selection and classification to classify whether the given input mammogram image is normal or abnormal. The method is applied to MIAS database of 322 film mammograms. The performance of the CAD system is analysed using Receiver Operating Characteristic (ROC) curve. This curve indicates the trade-offs between sensitivity and specificity that is available from a diagnostic system, and thus describes the inherent discrimination capacity of the proposed system. The result shows that the area under the ROC curve of the proposed algorithm is 0.985 with a sensitivity of 98.1% and specificity of 92.105%. Experimental results demonstrate that the proposed method can form an effective CAD system, and achieve good classification accuracy.
Performance Comparison Analysis for Medical Images Using Deep Learning Approa...IRJET Journal
This document discusses and compares several deep learning approaches for analyzing medical images, specifically chest x-rays. It first provides an abstract that outlines comparing existing technologies for analyzing chest x-rays using deep learning. It then reviews literature on models like convolutional neural networks (CNN), fully convolutional networks (FCN), lookup-based convolutional neural networks (LCNN), and deep cascade of convolutional neural networks (DCCNN) that have been applied to tasks like image segmentation, classification, and quality assessment of medical images. The document compares the performance of these models on different medical image datasets based on accuracy metrics.
Early detection of breast cancer using mammography images and software engine...TELKOMNIKA JOURNAL
The breast cancer has affected a wide region of women as a particular case. Therefore, different researchers have focused on the early detection of this disease to overcome it in efficient way. In this paper, an early breast cancer detection system has been proposed based on mammography images. The proposed system adopts deep-learning technique to increase the accuracy of detection. The convolutional neural network (CNN) model is considered for preparing the datasets of training and test. It is important to note that the software engineering process model has been adopted in constructing the proposed algorithm. This is to increase the reliably, flexibility and extendibility of the system. The user interfaces of the system are designed as a website used at country side general purpose (GP) health centers for early detection to the disease under lacking in specialist medical staff. The obtained results show the efficiency of the proposed system in terms of accuracy up to more than 90% and decrease the efforts of medical staff as well as helping the patients. As a conclusion, the proposed system can help patients by early detecting the breast cancer at far places from hospital and referring them to nearest specialist center.
Modified fuzzy rough set technique with stacked autoencoder model for magneti...IJECEIAES
Breast cancer is the common cancer in women, where early detection reduces the mortality rate. The magnetic resonance imaging (MRI) images are efficient in analyzing breast cancer, but it is hard to identify the abnormalities. The manual breast cancer detection in MRI images is inefficient; therefore, a deep learning-based system is implemented in this manuscript. Initially, the visual quality improvement is done using region growing and adaptive histogram equalization (AHE), and then, the breast lesion is segmented by Otsu thresholding with morphological transform. Next, the features are extracted from the segmented lesion, and a modified fuzzy rough set technique is proposed to reduce the dimensions of the extracted features that decreases the system complexity and computational time. The active features are fed to the stacked autoencoder for classifying the benign and malignant classes. The results demonstrated that the proposed model attained 99% and 99.22% of classification accuracy on the benchmark datasets, which are higher related to the comparative classifiers: decision tree, naïve Bayes, random forest and k-nearest neighbor (KNN). The obtained results state that the proposed model superiorly screens and detects the breast lesions that assists clinicians in effective therapeutic intervention and timely treatment.
Detection of Skin Diseases based on Skin lesion imagesIRJET Journal
This document discusses a study on detecting skin diseases from skin lesion images using deep learning algorithms. The researchers collected a dataset of skin images from the International Skin Imaging Collaboration repository. They used two deep neural networks - a convolutional neural network (CNN) and ResNet50 - to classify the images as malignant, benign, or normal. The CNN and ResNet50 models were trained on 80% of the dataset and tested on the remaining 20%. The results showed that the deep learning algorithms could accurately identify different types of skin cancers and diseases from images, which could help in the early detection of skin cancer.
Detection of Breast Cancer using BPN Classifier in MammogramsIRJET Journal
This document presents a method for detecting breast cancer in mammograms using a Back Propagation Network (BPN) classifier. The method involves preprocessing mammogram images, extracting Grey Level Co-occurrence Matrix (GLCM) texture features from wavelet sub-bands of the images, and training a BPN classifier on the features to classify mammograms as normal or abnormal. The BPN classifier is trained using a backpropagation algorithm to minimize error and accurately classify mammograms based on the extracted GLCM features. Experimental results found the method achieved a sensitivity of 100%, specificity of 75%, and accuracy of 90.91% for breast cancer detection and classification in mammograms.
Breast cancer detection using ensemble of convolutional neural networksIJECEIAES
Early detection leading to timely treatment in the initial stages of cancer may decrease the breast cancer death rate. We propose deep learning techniques along with image processing for the detection of tumors. The availability of online datasets and advances in graphical processing units (GPU) have promoted the application of deep learning models for the detection of breast cancer. In this paper, deep learning models using convolutional neural network (CNN) have been built to automatically classify mammograms into benign and malignant. Issues like overfitting and dataset imbalance are overcome. Experimentation has been done on two publicly available datasets, namely mammographic image analysis society (MIAS) database and digital database for screening mammography (DDSM). Robustness of the models is accomplished by merging the datasets. In our experimentation, MatConvNet has achieved an accuracy of 94.2% on the merged dataset, performing the best amongst all the CNN models used individually. Hungarian optimization algorithm is employed for selection of individual CNN models to form an ensemble. Ensemble of CNN models led to an improved performance, resulting in an accuracy of 95.7%.
The document describes using a convolutional neural network with the VGG16 architecture to classify lung cancer CT scan images into 4 classes: large cell carcinoma, squamous cell carcinoma, adenocarcinoma, and normal lungs. The model is trained on a dataset of 1000 CT scan images from Kaggle and achieves an AUC of 0.94, indicating high accuracy in identifying different types of lung cancer. This CNN model with pre-trained VGG16 weights provides an effective approach for classifying lung cancer images and could help enable early diagnosis and treatment of lung cancer.
An intelligent mammogram diagnosis system can be very helpful for radiologist in detecting the abnormalities earlier than typical screening techniques. This paper investigates a new classification approach for detection of breast abnormalities in digital mammograms using League Championship Algorithm Optimized Ensembled Fully Complex valued Relaxation Network (LCA-FCRN). The proposed algorithm is based on extracting curvelet fractal texture features from the mammograms and classifying the suspicious regions by applying a pattern classifier. The whole system includes steps for pre-processing, feature extraction, feature selection and classification to classify whether the given input mammogram image is normal or abnormal. The method is applied to MIAS database of 322 film mammograms. The performance of the CAD system is analysed using Receiver Operating Characteristic (ROC) curve. This curve indicates the trade-offs between sensitivity and specificity that is available from a diagnostic system, and thus describes the inherent discrimination capacity of the proposed system. The result shows that the area under the ROC curve of the proposed algorithm is 0.985 with a sensitivity of 98.1% and specificity of 92.105%. Experimental results demonstrate that the proposed method can form an effective CAD system, and achieve good classification accuracy.
Performance Comparison Analysis for Medical Images Using Deep Learning Approa...IRJET Journal
This document discusses and compares several deep learning approaches for analyzing medical images, specifically chest x-rays. It first provides an abstract that outlines comparing existing technologies for analyzing chest x-rays using deep learning. It then reviews literature on models like convolutional neural networks (CNN), fully convolutional networks (FCN), lookup-based convolutional neural networks (LCNN), and deep cascade of convolutional neural networks (DCCNN) that have been applied to tasks like image segmentation, classification, and quality assessment of medical images. The document compares the performance of these models on different medical image datasets based on accuracy metrics.
Early detection of breast cancer using mammography images and software engine...TELKOMNIKA JOURNAL
The breast cancer has affected a wide region of women as a particular case. Therefore, different researchers have focused on the early detection of this disease to overcome it in efficient way. In this paper, an early breast cancer detection system has been proposed based on mammography images. The proposed system adopts deep-learning technique to increase the accuracy of detection. The convolutional neural network (CNN) model is considered for preparing the datasets of training and test. It is important to note that the software engineering process model has been adopted in constructing the proposed algorithm. This is to increase the reliably, flexibility and extendibility of the system. The user interfaces of the system are designed as a website used at country side general purpose (GP) health centers for early detection to the disease under lacking in specialist medical staff. The obtained results show the efficiency of the proposed system in terms of accuracy up to more than 90% and decrease the efforts of medical staff as well as helping the patients. As a conclusion, the proposed system can help patients by early detecting the breast cancer at far places from hospital and referring them to nearest specialist center.
Modified fuzzy rough set technique with stacked autoencoder model for magneti...IJECEIAES
Breast cancer is the common cancer in women, where early detection reduces the mortality rate. The magnetic resonance imaging (MRI) images are efficient in analyzing breast cancer, but it is hard to identify the abnormalities. The manual breast cancer detection in MRI images is inefficient; therefore, a deep learning-based system is implemented in this manuscript. Initially, the visual quality improvement is done using region growing and adaptive histogram equalization (AHE), and then, the breast lesion is segmented by Otsu thresholding with morphological transform. Next, the features are extracted from the segmented lesion, and a modified fuzzy rough set technique is proposed to reduce the dimensions of the extracted features that decreases the system complexity and computational time. The active features are fed to the stacked autoencoder for classifying the benign and malignant classes. The results demonstrated that the proposed model attained 99% and 99.22% of classification accuracy on the benchmark datasets, which are higher related to the comparative classifiers: decision tree, naïve Bayes, random forest and k-nearest neighbor (KNN). The obtained results state that the proposed model superiorly screens and detects the breast lesions that assists clinicians in effective therapeutic intervention and timely treatment.
Detection of Skin Diseases based on Skin lesion imagesIRJET Journal
This document discusses a study on detecting skin diseases from skin lesion images using deep learning algorithms. The researchers collected a dataset of skin images from the International Skin Imaging Collaboration repository. They used two deep neural networks - a convolutional neural network (CNN) and ResNet50 - to classify the images as malignant, benign, or normal. The CNN and ResNet50 models were trained on 80% of the dataset and tested on the remaining 20%. The results showed that the deep learning algorithms could accurately identify different types of skin cancers and diseases from images, which could help in the early detection of skin cancer.
Detection of Breast Cancer using BPN Classifier in MammogramsIRJET Journal
This document presents a method for detecting breast cancer in mammograms using a Back Propagation Network (BPN) classifier. The method involves preprocessing mammogram images, extracting Grey Level Co-occurrence Matrix (GLCM) texture features from wavelet sub-bands of the images, and training a BPN classifier on the features to classify mammograms as normal or abnormal. The BPN classifier is trained using a backpropagation algorithm to minimize error and accurately classify mammograms based on the extracted GLCM features. Experimental results found the method achieved a sensitivity of 100%, specificity of 75%, and accuracy of 90.91% for breast cancer detection and classification in mammograms.
Breast cancer detection using ensemble of convolutional neural networksIJECEIAES
Early detection leading to timely treatment in the initial stages of cancer may decrease the breast cancer death rate. We propose deep learning techniques along with image processing for the detection of tumors. The availability of online datasets and advances in graphical processing units (GPU) have promoted the application of deep learning models for the detection of breast cancer. In this paper, deep learning models using convolutional neural network (CNN) have been built to automatically classify mammograms into benign and malignant. Issues like overfitting and dataset imbalance are overcome. Experimentation has been done on two publicly available datasets, namely mammographic image analysis society (MIAS) database and digital database for screening mammography (DDSM). Robustness of the models is accomplished by merging the datasets. In our experimentation, MatConvNet has achieved an accuracy of 94.2% on the merged dataset, performing the best amongst all the CNN models used individually. Hungarian optimization algorithm is employed for selection of individual CNN models to form an ensemble. Ensemble of CNN models led to an improved performance, resulting in an accuracy of 95.7%.
Deep segmentation of the liver and the hepatic tumors from abdomen tomography...IJECEIAES
A pipelined framework is proposed for accurate, automated, simultaneous segmentation of the liver as well as the hepatic tumors from computed tomography (CT) images. The introduced framework composed of three pipelined levels. First, two different transfers deep convolutional neural networks (CNN) are applied to get high-level compact features of CT images. Second, a pixel-wise classifier is used to obtain two outputclassified maps for each CNN model. Finally, a fusion neural network (FNN) is used to integrate the two maps. Experimentations performed on the MICCAI’2017 database of the liver tumor segmentation (LITS) challenge, result in a dice similarity coefficient (DSC) of 93.5% for the segmentation of the liver and of 74.40% for the segmentation of the lesion, using a 5-fold cross-validation scheme. Comparative results with the state-of-the-art techniques on the same data show the competing performance of the proposed framework for simultaneous liver and tumor segmentation.
Preliminary Lung Cancer Detection using Deep Neural NetworksIRJET Journal
This document presents a study on using deep learning techniques for preliminary lung cancer detection. Specifically, it proposes using a convolutional neural network (CNN) model for classifying histopathological lung cancer tissue images. The study describes the dataset used, which contains labeled RGB images of cancerous and non-cancerous lung tissue. It then discusses the proposed CNN architecture, which includes convolutional, pooling, dropout and fully connected layers. The model is trained on the dataset for 30 epochs and achieves 96.43% accuracy on the training set and 97.10% accuracy on the validation set, indicating it generalizes well for lung cancer classification. In conclusion, the CNN model shows promising results for preliminary lung cancer detection from histopathological images.
Classification of mammograms based on features extraction techniques using su...CSITiaesprime
Now mammography can be defined as the most reliable method for early breast cancer detection. The main goal of this study is to design a classifier model to help radiologists to provide a second view to diagnose mammograms. In the proposed system medium filter and binary image with a global threshold have been applied for removing the noise and small artifacts in the preprocessing stage. Secondly, in the segmentation phase, a hybrid bounding box and region growing (HBBRG) algorithm are utilizing to remove pectoral muscles, and then a geometric method has been applied to cut the largest possible square that can be obtained from a mammogram which represents the region of interest (ROI). In the features extraction phase three method was used to prepare texture features to be a suitable introduction to the classification process are first Order (statistical features), local binary patterns (LBP), and gray-level co-occurrence matrix (GLCM), Finally, support vector machine (SVM) has been applied in two-level to classify mammogram images in the first level to normal or abnormal, and then the classification of abnormal once in the second level to the benign or malignant image. The system was tested on the MAIS the mammogram image analysis society (MIAS) database, in addition to the image from the Teaching Oncology Hospital, Medical City in Baghdad, where the results showed achieving an accuracy of 95.454% for the first level and 97.260% for the second level, also, the results of applying the proposed system to the MIAS database alone were achieving an accuracy of 93.105% for the first level and 94.59 for the second level.
Melanoma Skin Cancer Detection using Deep LearningIRJET Journal
This document presents research on developing a deep learning model to detect melanoma skin cancer. The researchers created a convolutional neural network called Xception to analyze images of skin lesions and classify them as benign or malignant. They developed a web application using Flask that allows users to upload images for analysis. The Xception model achieved 97% accuracy on a test dataset. The web app was also able to accurately classify images, demonstrating its potential to assist dermatologists in early detection of melanoma skin cancer. However, further improvements are still needed before the model and web app can be fully relied upon for clinical diagnosis.
IRJET- A Survey on Medical Image Interpretation for Predicting PneumoniaIRJET Journal
This document summarizes research on using machine learning and deep learning techniques to interpret medical images and predict pneumonia. It first discusses how medical image analysis is an active field for machine learning. It then reviews several related studies on using convolutional neural networks (CNNs) and transfer learning to classify chest x-rays and detect pneumonia. Specifically, it examines research on developing CNN models for pneumonia classification and using pre-trained CNN architectures like VGG16, VGG19, and ResNet with transfer learning. The document concludes that computer-aided diagnosis systems using deep learning can provide accurate predictions to assist radiologists in pneumonia diagnosis from chest x-rays.
The document describes a study that used a convolutional neural network with a ConvNeXtLarge architecture to classify skin cancer images into benign and malignant classes. The CNN model was trained on a dataset of 3,297 skin cancer images from Kaggle. It achieved an AUC of 0.91 for classifying the images, demonstrating the ConvNeXtLarge architecture is effective for this task. The study aims to help early diagnosis and treatment of skin cancers.
Deep Learning based Multi-class Brain Tumor ClassificationIRJET Journal
The document discusses a study that aims to improve the classification of brain tumors on MRI images using deep learning techniques. It compares several convolutional neural network architectures (Custom CNN, DenseNet169, MobileNet, VGG-16, and ResNet152) for multi-class brain tumor classification using MRI data. The models are trained on a dataset of approximately 5,000 brain MR images and their performance at tumor detection is evaluated and compared. Transfer learning techniques are also discussed for applying knowledge from one task to improve predictions for new tasks.
Skin Disease Detection using Convolutional Neural NetworkIRJET Journal
This document summarizes a research paper that proposes a skin disease detection system using a convolutional neural network. The system aims to identify seven types of skin cancers using the HAM10000 dataset from Kaggle containing over 10,000 dermoscopic images. The researchers first preprocess the images and handle class imbalance before training a CNN model with max pooling, batch normalization, dropout and an Adam optimizer. The trained model achieved an accuracy of 74-75% on the test set at 50 epochs. In conclusion, the CNN model showed promising accuracy for skin disease detection but could be improved with a more reliable dataset and parameter tuning.
Twin support vector machine using kernel function for colorectal cancer detec...journalBEEI
Nowadays, machine learning technology is needed in the medical field. therefore, this research is useful for solving problems in the medical field by using machine learning. Many cases of colorectal cancer are diagnosed late. When colorectal cancer is detected, the cancer is usually well developed. Machine learning is an approach that is part of artificial intelligence and can detect colorectal cancer early. This study discusses colorectal cancer detection using twin support vector machine (SVM) method and kernel function i.e. linear kernels, polynomial kernels, RBF kernels, and gaussian kernels. By comparing the accuracy and running time, then we will know which method is better in classifying the colorectal cancer dataset that we get from Al-Islam Hospital, Bandung, Indonesia. The results showed that polynomial kernels has better accuracy and running time. It can be seen with a maximum accuracy of twin SVM using polynomial kernels 86% and 0.502 seconds running time.
A Review Paper on Automated Brain Tumor DetectionIRJET Journal
This document reviews several studies that used deep learning techniques to detect brain tumors using MRI images. It summarizes key papers that used algorithms like VGG-16, ResNet, Inception, Fast R-CNN and more. VGG-16 achieved accuracies of 75.18-89.45% for detecting glioma, meningioma and pituitary tumors. ResNet-50 and Inception-V3 also performed well with 96% accuracy. Later papers compared methods like AlexNet, GoogleNet and ResNet, with VGG-16 achieving the highest accuracy of 98.69%. More recent multi-channel approaches using DenseNet201, ResNet-50 and SRN obtained 98.31% accuracy. Overall, deep learning has shown good results for brain
Breast cancer histological images nuclei segmentation and optimized classifi...IJECEIAES
This document summarizes a research paper that proposes a deep learning method for breast cancer histological image segmentation and classification. The method uses a feature pyramid network to extract features from histological images, which are then optimized using a grasshopper optimization algorithm. The optimized features are used with a segmenting objects by locations convolutional network for nucleus segmentation. For classification, the features are passed through fully connected layers. The method achieves 88.46% segmentation accuracy and 99.2% classification accuracy on a large breast cancer image dataset. It provides an effective computer-aided approach for breast cancer analysis in a medical setting.
A deep convolutional structure-based approach for accurate recognition of ski...IJECEIAES
One-third of all cancer diagnoses worldwide are skin malignancies. One of the most common tumors, skin cancer can develop from a variety of dermatological conditions and is subdivided into different categories based on its textile, color, body, and other morphological characteristics. The most effective strategy to lower the mortality rate of melanoma is early identification because skin cancer incidence has been on the rise recently. In order to categorize dermoscopy images into the four diagnosis classifications of melanoma, benign, malignant, and human against machine (HAM) not melanoma, this research suggests a computer-aided diagnosis (CAD) system. Experimental results show that the suggested approach enabled 97.25% classification accuracy. In order to automate the identification of skin cancer and expedite the diagnosis process in order to save a life, the proposed technique offers a less complex and cutting-edge framework.
Glioblastomas brain tumour segmentation based on convolutional neural network...IJECEIAES
Brain tumour segmentation can improve diagnostics efficiency, rise the prediction rate and treatment planning. This will help the doctors and experts in their work. Where many types of brain tumour may be classified easily, the gliomas tumour is challenging to be segmented because of the diffusion between the tumour and the surrounding edema. Another important challenge with this type of brain tumour is that the tumour may grow anywhere in the brain with different shape and size. Brain cancer presents one of the most famous diseases over the world, which encourage the researchers to find a high-throughput system for tumour detection and classification. Several approaches have been proposed to design automatic detection and classification systems. This paper presents an integrated framework to segment the gliomas brain tumour automatically using pixel clustering for the MRI images foreground and background and classify its type based on deep learning mechanism, which is the convolutional neural network. In this work, a novel segmentation and classification system is proposed to detect the tumour cells and classify the brain image if it is healthy or not. After collecting data for healthy and non-healthy brain images, satisfactory results are found and registered using computer vision approaches. This approach can be used as a part of a bigger diagnosis system for breast tumour detection and manipulation.
Lung Cancer Detection using transfer learning.pptx.pdfjagan477830
Lung cancer is one of the deadliest cancers worldwide. However, the early detection of lung cancer significantly improves survival rate. Cancerous (malignant) and noncancerous (benign) pulmonary nodules are the small growths of cells inside the lung. Detection of malignant lung nodules at an early stage is necessary for the crucial prognosis.
A Survey of Convolutional Neural Network Architectures for Deep Learning via ...ijtsrd
Convolutional Neural Network CNN designs can successfully classify, predict and cluster in many artificial intelligence applications. In the health sector, intensive studies continue for disease classification. When the literature in this field is examined, it is seen that the studies are concentrated on the health sector. Thanks to these studies, doctors can make an accurate diagnosis by examining radiological images more consistently. In addition, doctors can save time to do other patient work by using CNN. In this study, related current manuscripts in the health sector were examined. The contributions of these publications to the literature were explained and evaluated. Complementary and contradictory arguments of the presented perspectives were revealed. It has been stated that the current status of the studies carried out and in which direction the future studies should evolve and that they can make an important contribution to the literature. Suggestions have been made for the guidance for future studies. Ahmet Özcan | Mahmut Ünver | Atilla Ergüzen "A Survey of Convolutional Neural Network Architectures for Deep Learning via Health Images" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-2 , February 2022, URL: https://www.ijtsrd.com/papers/ijtsrd49156.pdf Paper URL: https://www.ijtsrd.com/computer-science/artificial-intelligence/49156/a-survey-of-convolutional-neural-network-architectures-for-deep-learning-via-health-images/ahmet-özcan
The document presents a proposal for developing a hybrid machine learning model for prostate cancer detection. It aims to combine deep convolutional neural networks (DCNN) and fuzzy support vector machines (SVMs) to overcome limitations of individual models. The methodology section outlines the steps as: pre-processing data, extracting features using DCNN, training and testing the fuzzy SVM classifier, and evaluating performance. Key aspects of the DCNN and fuzzy SVM approaches are also summarized, such as convolutional and pooling layers, fully connected layers, and the SVM classification technique. The proposal seeks to improve prostate cancer detection accuracy through this hybrid modeling approach.
A new model for large dataset dimensionality reduction based on teaching lear...TELKOMNIKA JOURNAL
One of the human diseases with a high rate of mortality each year is breast cancer (BC). Among all the forms of cancer, BC is the commonest cause of death among women globally. Some of the effective ways of data classification are data mining and classification methods. These methods are particularly efficient in the medical field due to the presence of irrelevant and redundant attributes in medical datasets. Such redundant attributes are not needed to obtain an accurate estimation of disease diagnosis. Teaching learning-based optimization (TLBO) is a new metaheuristic that has been successfully applied to several intractable optimization problems in recent years. This paper presents the use of a multi-objective TLBO algorithm for the selection of feature subsets in automatic BC diagnosis. For the classification task in this work, the logistic regression (LR) method was deployed. From the results, the projected method produced better BC dataset classification accuracy (classified into malignant and benign). This result showed that the projected TLBO is an efficient features optimization technique for sustaining data-based decision-making systems.
Computer Aided System for Detection and Classification of Breast CancerIJITCA Journal
Breast cancer is one of the most important causes of death among all type of cancers for grown-up and
older women, mainly in developed countries, and its rate is rising. Since the cause of this disease is not yet
known, early detection is the best way to decrease the breast cancer mortality. At present, early detection of
breast cancer is attained by means of mammography. An intelligent computer-aided diagnosis system can
be very helpful for radiologist in detecting and diagnosing cancerous cell patterns earlier and faster than
typical screening programs. This paper proposes a computer aided system for automatic detection and
classification of breast cancer in mammogram images. Intuitionistic Fuzzy C-Means clustering technique
has been used to identify the suspicious region or the Region of Interest automatically. Then, the feature
data base is designed using histogram features, Gray Level Concurrence wavelet features and wavelet
energy features. Finally, the feature database is submitted to self-adaptive resource allocation network
classifier for classification of mammogram image as normal, benign or malignant. The proposed system is
verified with 322 mammograms from the Mammographic Image Analysis Society Database. The results
show that the proposed system produces better results.
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.
IRJET - Classification of Cancer Images using Deep LearningIRJET Journal
This document presents a methodology for classifying breast cancer histopathology images using deep learning. Specifically, it aims to classify images as either invasive ductal carcinoma (IDC) or non-IDC using a convolutional neural network (CNN) model. The proposed methodology involves preprocessing the images, building a CNN with convolutional, pooling and fully connected layers, training the model on labeled image data, and using the trained model to classify new images as IDC or non-IDC. The goal is to develop an automated system for early and accurate detection of breast cancer subtypes to improve diagnosis and patient outcomes.
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.
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Deep segmentation of the liver and the hepatic tumors from abdomen tomography...IJECEIAES
A pipelined framework is proposed for accurate, automated, simultaneous segmentation of the liver as well as the hepatic tumors from computed tomography (CT) images. The introduced framework composed of three pipelined levels. First, two different transfers deep convolutional neural networks (CNN) are applied to get high-level compact features of CT images. Second, a pixel-wise classifier is used to obtain two outputclassified maps for each CNN model. Finally, a fusion neural network (FNN) is used to integrate the two maps. Experimentations performed on the MICCAI’2017 database of the liver tumor segmentation (LITS) challenge, result in a dice similarity coefficient (DSC) of 93.5% for the segmentation of the liver and of 74.40% for the segmentation of the lesion, using a 5-fold cross-validation scheme. Comparative results with the state-of-the-art techniques on the same data show the competing performance of the proposed framework for simultaneous liver and tumor segmentation.
Preliminary Lung Cancer Detection using Deep Neural NetworksIRJET Journal
This document presents a study on using deep learning techniques for preliminary lung cancer detection. Specifically, it proposes using a convolutional neural network (CNN) model for classifying histopathological lung cancer tissue images. The study describes the dataset used, which contains labeled RGB images of cancerous and non-cancerous lung tissue. It then discusses the proposed CNN architecture, which includes convolutional, pooling, dropout and fully connected layers. The model is trained on the dataset for 30 epochs and achieves 96.43% accuracy on the training set and 97.10% accuracy on the validation set, indicating it generalizes well for lung cancer classification. In conclusion, the CNN model shows promising results for preliminary lung cancer detection from histopathological images.
Classification of mammograms based on features extraction techniques using su...CSITiaesprime
Now mammography can be defined as the most reliable method for early breast cancer detection. The main goal of this study is to design a classifier model to help radiologists to provide a second view to diagnose mammograms. In the proposed system medium filter and binary image with a global threshold have been applied for removing the noise and small artifacts in the preprocessing stage. Secondly, in the segmentation phase, a hybrid bounding box and region growing (HBBRG) algorithm are utilizing to remove pectoral muscles, and then a geometric method has been applied to cut the largest possible square that can be obtained from a mammogram which represents the region of interest (ROI). In the features extraction phase three method was used to prepare texture features to be a suitable introduction to the classification process are first Order (statistical features), local binary patterns (LBP), and gray-level co-occurrence matrix (GLCM), Finally, support vector machine (SVM) has been applied in two-level to classify mammogram images in the first level to normal or abnormal, and then the classification of abnormal once in the second level to the benign or malignant image. The system was tested on the MAIS the mammogram image analysis society (MIAS) database, in addition to the image from the Teaching Oncology Hospital, Medical City in Baghdad, where the results showed achieving an accuracy of 95.454% for the first level and 97.260% for the second level, also, the results of applying the proposed system to the MIAS database alone were achieving an accuracy of 93.105% for the first level and 94.59 for the second level.
Melanoma Skin Cancer Detection using Deep LearningIRJET Journal
This document presents research on developing a deep learning model to detect melanoma skin cancer. The researchers created a convolutional neural network called Xception to analyze images of skin lesions and classify them as benign or malignant. They developed a web application using Flask that allows users to upload images for analysis. The Xception model achieved 97% accuracy on a test dataset. The web app was also able to accurately classify images, demonstrating its potential to assist dermatologists in early detection of melanoma skin cancer. However, further improvements are still needed before the model and web app can be fully relied upon for clinical diagnosis.
IRJET- A Survey on Medical Image Interpretation for Predicting PneumoniaIRJET Journal
This document summarizes research on using machine learning and deep learning techniques to interpret medical images and predict pneumonia. It first discusses how medical image analysis is an active field for machine learning. It then reviews several related studies on using convolutional neural networks (CNNs) and transfer learning to classify chest x-rays and detect pneumonia. Specifically, it examines research on developing CNN models for pneumonia classification and using pre-trained CNN architectures like VGG16, VGG19, and ResNet with transfer learning. The document concludes that computer-aided diagnosis systems using deep learning can provide accurate predictions to assist radiologists in pneumonia diagnosis from chest x-rays.
The document describes a study that used a convolutional neural network with a ConvNeXtLarge architecture to classify skin cancer images into benign and malignant classes. The CNN model was trained on a dataset of 3,297 skin cancer images from Kaggle. It achieved an AUC of 0.91 for classifying the images, demonstrating the ConvNeXtLarge architecture is effective for this task. The study aims to help early diagnosis and treatment of skin cancers.
Deep Learning based Multi-class Brain Tumor ClassificationIRJET Journal
The document discusses a study that aims to improve the classification of brain tumors on MRI images using deep learning techniques. It compares several convolutional neural network architectures (Custom CNN, DenseNet169, MobileNet, VGG-16, and ResNet152) for multi-class brain tumor classification using MRI data. The models are trained on a dataset of approximately 5,000 brain MR images and their performance at tumor detection is evaluated and compared. Transfer learning techniques are also discussed for applying knowledge from one task to improve predictions for new tasks.
Skin Disease Detection using Convolutional Neural NetworkIRJET Journal
This document summarizes a research paper that proposes a skin disease detection system using a convolutional neural network. The system aims to identify seven types of skin cancers using the HAM10000 dataset from Kaggle containing over 10,000 dermoscopic images. The researchers first preprocess the images and handle class imbalance before training a CNN model with max pooling, batch normalization, dropout and an Adam optimizer. The trained model achieved an accuracy of 74-75% on the test set at 50 epochs. In conclusion, the CNN model showed promising accuracy for skin disease detection but could be improved with a more reliable dataset and parameter tuning.
Twin support vector machine using kernel function for colorectal cancer detec...journalBEEI
Nowadays, machine learning technology is needed in the medical field. therefore, this research is useful for solving problems in the medical field by using machine learning. Many cases of colorectal cancer are diagnosed late. When colorectal cancer is detected, the cancer is usually well developed. Machine learning is an approach that is part of artificial intelligence and can detect colorectal cancer early. This study discusses colorectal cancer detection using twin support vector machine (SVM) method and kernel function i.e. linear kernels, polynomial kernels, RBF kernels, and gaussian kernels. By comparing the accuracy and running time, then we will know which method is better in classifying the colorectal cancer dataset that we get from Al-Islam Hospital, Bandung, Indonesia. The results showed that polynomial kernels has better accuracy and running time. It can be seen with a maximum accuracy of twin SVM using polynomial kernels 86% and 0.502 seconds running time.
A Review Paper on Automated Brain Tumor DetectionIRJET Journal
This document reviews several studies that used deep learning techniques to detect brain tumors using MRI images. It summarizes key papers that used algorithms like VGG-16, ResNet, Inception, Fast R-CNN and more. VGG-16 achieved accuracies of 75.18-89.45% for detecting glioma, meningioma and pituitary tumors. ResNet-50 and Inception-V3 also performed well with 96% accuracy. Later papers compared methods like AlexNet, GoogleNet and ResNet, with VGG-16 achieving the highest accuracy of 98.69%. More recent multi-channel approaches using DenseNet201, ResNet-50 and SRN obtained 98.31% accuracy. Overall, deep learning has shown good results for brain
Breast cancer histological images nuclei segmentation and optimized classifi...IJECEIAES
This document summarizes a research paper that proposes a deep learning method for breast cancer histological image segmentation and classification. The method uses a feature pyramid network to extract features from histological images, which are then optimized using a grasshopper optimization algorithm. The optimized features are used with a segmenting objects by locations convolutional network for nucleus segmentation. For classification, the features are passed through fully connected layers. The method achieves 88.46% segmentation accuracy and 99.2% classification accuracy on a large breast cancer image dataset. It provides an effective computer-aided approach for breast cancer analysis in a medical setting.
A deep convolutional structure-based approach for accurate recognition of ski...IJECEIAES
One-third of all cancer diagnoses worldwide are skin malignancies. One of the most common tumors, skin cancer can develop from a variety of dermatological conditions and is subdivided into different categories based on its textile, color, body, and other morphological characteristics. The most effective strategy to lower the mortality rate of melanoma is early identification because skin cancer incidence has been on the rise recently. In order to categorize dermoscopy images into the four diagnosis classifications of melanoma, benign, malignant, and human against machine (HAM) not melanoma, this research suggests a computer-aided diagnosis (CAD) system. Experimental results show that the suggested approach enabled 97.25% classification accuracy. In order to automate the identification of skin cancer and expedite the diagnosis process in order to save a life, the proposed technique offers a less complex and cutting-edge framework.
Glioblastomas brain tumour segmentation based on convolutional neural network...IJECEIAES
Brain tumour segmentation can improve diagnostics efficiency, rise the prediction rate and treatment planning. This will help the doctors and experts in their work. Where many types of brain tumour may be classified easily, the gliomas tumour is challenging to be segmented because of the diffusion between the tumour and the surrounding edema. Another important challenge with this type of brain tumour is that the tumour may grow anywhere in the brain with different shape and size. Brain cancer presents one of the most famous diseases over the world, which encourage the researchers to find a high-throughput system for tumour detection and classification. Several approaches have been proposed to design automatic detection and classification systems. This paper presents an integrated framework to segment the gliomas brain tumour automatically using pixel clustering for the MRI images foreground and background and classify its type based on deep learning mechanism, which is the convolutional neural network. In this work, a novel segmentation and classification system is proposed to detect the tumour cells and classify the brain image if it is healthy or not. After collecting data for healthy and non-healthy brain images, satisfactory results are found and registered using computer vision approaches. This approach can be used as a part of a bigger diagnosis system for breast tumour detection and manipulation.
Lung Cancer Detection using transfer learning.pptx.pdfjagan477830
Lung cancer is one of the deadliest cancers worldwide. However, the early detection of lung cancer significantly improves survival rate. Cancerous (malignant) and noncancerous (benign) pulmonary nodules are the small growths of cells inside the lung. Detection of malignant lung nodules at an early stage is necessary for the crucial prognosis.
A Survey of Convolutional Neural Network Architectures for Deep Learning via ...ijtsrd
Convolutional Neural Network CNN designs can successfully classify, predict and cluster in many artificial intelligence applications. In the health sector, intensive studies continue for disease classification. When the literature in this field is examined, it is seen that the studies are concentrated on the health sector. Thanks to these studies, doctors can make an accurate diagnosis by examining radiological images more consistently. In addition, doctors can save time to do other patient work by using CNN. In this study, related current manuscripts in the health sector were examined. The contributions of these publications to the literature were explained and evaluated. Complementary and contradictory arguments of the presented perspectives were revealed. It has been stated that the current status of the studies carried out and in which direction the future studies should evolve and that they can make an important contribution to the literature. Suggestions have been made for the guidance for future studies. Ahmet Özcan | Mahmut Ünver | Atilla Ergüzen "A Survey of Convolutional Neural Network Architectures for Deep Learning via Health Images" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-2 , February 2022, URL: https://www.ijtsrd.com/papers/ijtsrd49156.pdf Paper URL: https://www.ijtsrd.com/computer-science/artificial-intelligence/49156/a-survey-of-convolutional-neural-network-architectures-for-deep-learning-via-health-images/ahmet-özcan
The document presents a proposal for developing a hybrid machine learning model for prostate cancer detection. It aims to combine deep convolutional neural networks (DCNN) and fuzzy support vector machines (SVMs) to overcome limitations of individual models. The methodology section outlines the steps as: pre-processing data, extracting features using DCNN, training and testing the fuzzy SVM classifier, and evaluating performance. Key aspects of the DCNN and fuzzy SVM approaches are also summarized, such as convolutional and pooling layers, fully connected layers, and the SVM classification technique. The proposal seeks to improve prostate cancer detection accuracy through this hybrid modeling approach.
A new model for large dataset dimensionality reduction based on teaching lear...TELKOMNIKA JOURNAL
One of the human diseases with a high rate of mortality each year is breast cancer (BC). Among all the forms of cancer, BC is the commonest cause of death among women globally. Some of the effective ways of data classification are data mining and classification methods. These methods are particularly efficient in the medical field due to the presence of irrelevant and redundant attributes in medical datasets. Such redundant attributes are not needed to obtain an accurate estimation of disease diagnosis. Teaching learning-based optimization (TLBO) is a new metaheuristic that has been successfully applied to several intractable optimization problems in recent years. This paper presents the use of a multi-objective TLBO algorithm for the selection of feature subsets in automatic BC diagnosis. For the classification task in this work, the logistic regression (LR) method was deployed. From the results, the projected method produced better BC dataset classification accuracy (classified into malignant and benign). This result showed that the projected TLBO is an efficient features optimization technique for sustaining data-based decision-making systems.
Computer Aided System for Detection and Classification of Breast CancerIJITCA Journal
Breast cancer is one of the most important causes of death among all type of cancers for grown-up and
older women, mainly in developed countries, and its rate is rising. Since the cause of this disease is not yet
known, early detection is the best way to decrease the breast cancer mortality. At present, early detection of
breast cancer is attained by means of mammography. An intelligent computer-aided diagnosis system can
be very helpful for radiologist in detecting and diagnosing cancerous cell patterns earlier and faster than
typical screening programs. This paper proposes a computer aided system for automatic detection and
classification of breast cancer in mammogram images. Intuitionistic Fuzzy C-Means clustering technique
has been used to identify the suspicious region or the Region of Interest automatically. Then, the feature
data base is designed using histogram features, Gray Level Concurrence wavelet features and wavelet
energy features. Finally, the feature database is submitted to self-adaptive resource allocation network
classifier for classification of mammogram image as normal, benign or malignant. The proposed system is
verified with 322 mammograms from the Mammographic Image Analysis Society Database. The results
show that the proposed system produces better results.
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.
IRJET - Classification of Cancer Images using Deep LearningIRJET Journal
This document presents a methodology for classifying breast cancer histopathology images using deep learning. Specifically, it aims to classify images as either invasive ductal carcinoma (IDC) or non-IDC using a convolutional neural network (CNN) model. The proposed methodology involves preprocessing the images, building a CNN with convolutional, pooling and fully connected layers, training the model on labeled image data, and using the trained model to classify new images as IDC or non-IDC. The goal is to develop an automated system for early and accurate detection of breast cancer subtypes to improve diagnosis and patient outcomes.
Similar to Deep learning for cancer tumor classification using transfer learning and feature concatenation (20)
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
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Deep learning for cancer tumor classification using transfer learning and feature concatenation
1. International Journal of Electrical and Computer Engineering (IJECE)
Vol. 12, No. 6, December 2022, pp. 6736~6743
ISSN: 2088-8708, DOI: 10.11591/ijece.v12i6.pp6736-6743 6736
Journal homepage: http://ijece.iaescore.com
Deep learning for cancer tumor classification using transfer
learning and feature concatenation
Abdallah Mohamed Hassan1
, Mohamed Bakry El-Mashade1
, Ashraf Aboshosha2
1
Electrical Engineering Department, Faculty of Engineering, Al-Azhar University, Cairo, Egypt
2
NCRRT, Egyptian Atomic Energy Authority, Cairo, Egypt
Article Info ABSTRACT
Article history:
Received Sep 6, 2021
Revised Jun 19, 2022
Accepted Jul 15, 2022
Deep convolutional neural networks (CNNs) represent one of the
state-of-the-art methods for image classification in a variety of fields.
Because the number of training dataset images in biomedical image
classification is limited, transfer learning with CNNs is frequently applied.
Breast cancer is one of most common types of cancer that causes death in
women. Early detection and treatment of breast cancer are vital for
improving survival rates. In this paper, we propose a deep neural network
framework based on the transfer learning concept for detecting and
classifying breast cancer histopathology images. In the proposed framework,
we extract features from images using three pre-trained CNN architectures:
VGG-16, ResNet50, and Inception-v3, and concatenate their extracted
features, and then feed them into a fully connected (FC) layer to classify
benign and malignant tumor cells in the histopathology images of the breast
cancer. In comparison to the other CNN architectures that use a single CNN
and many conventional classification methods, the proposed framework
outperformed all other deep learning architectures and achieved an average
accuracy of 98.76%.
Keywords:
Breast cancer
Cancer tumor
Classification
Deep learning
Feature concatenation
Transfer learning
This is an open access article under the CC BY-SA license.
Corresponding Author:
Abdallah Mohamed Hassan
Electrical Engineering Department, Faculty of Engineering, Al-Azhar University
Cairo, Egypt
Email: abdallah.mohamed@azhar.edu.eg
1. INTRODUCTION
Analysis of the microscopic images that represent various human tissues has developed as one of the
most vital fields of biomedical research, as it aids in the understanding of a variety of biological processes.
Different applications of microscopic images classification have been developed, which include identifying
simple patient conditions and studying complex cell processes. Tissue image classification is extremely
important. After lung cancer, breast cancer is considering the most frequent cancer type studied and the most
prevalent type of cancer in women has the highest death rate in the world [1]. The radiologist uses
microscopic images of the breast to detect cancer indications in women at an early stage, and the rate of
survival will be increased if detected early. Pathologists use a microscope to analyze a sample of microscopic
images of breast tissue to detect and classify the types of cancer tumors, which are categorized into benign
and malignant tumor. The benign tumor is harmless, and the majority of this type is unable to become a
breast cancer source, while the malignant tumor is characterized by abnormal divisions and irregular growth.
Because manual classification of microscopic images is time-consuming and expensive, there is a
growing demand for automated systems as the rate of breast cancer rises and diagnosis differs. As a result,
computer-aided-diagnosis (CAD) system is required to decrease a specialist’s workload by increasing the
efficiency of diagnostic and reducing classification subjectivity. Various applications have been developed
2. Int J Elec & Comp Eng ISSN: 2088-8708
Deep learning for cancer tumor classification using transfer learning and … (Abdallah Mohamed Hassan)
6737
for microscopic image classification. Traditional automated classification techniques such as local binary
patterns (LBP) [2] that use hand-crafted features extractors, support vector machines (SVM) [3] as a linear
classifier, clustering-based algorithms segmentation and classification of nuclei [4], [5], hybrid SVM-ANN
[6]. Although these methods produced some acceptable results in classification, the accuracy might be
improved.
Deep convolutional neural networks were used to overcome the accuracy limits in traditional
machine learning techniques and have developed as one of the most advanced methods in the classification
process [7]. Deep CNN systems as nuclei detection and classification [8], tumor detection [9], skin disease
classification [10], detection and classification of lymph nodes metastasis [11]. On large datasets, CNN
systems perform well, but it fails on small datasets to achieve high gains.
The principle of transfer learning is used to exploit deep neural networks in small datasets to
enhance the CNN structure’s performance by combining their knowledge to reduce computing costs and
achieve high accuracy. CNN architecture is learned on a generic large dataset of nature images and then
employed as a features extractor using the pre-trained CNN structure in transfer learning. The generic
features extracted from the CNN can apply to various datasets [12], [13]. To improve transfer learning
performance, the use of a combination of multiple CNNs structures has been introduced and could eventually
replace the usage of a single CNN model. VGG16, ResNet50, and Inception-v3 networks have developed an
accurate and fast model for image classification [14]–[16], which are pre-trained on ImageNet.
In the suggested framework, we use transfer learning and a combination of extracted from multiple
CNN architectures to overcome the shortcomings in cancer tumor detection and classification in existing
systems. We can summarize the contributions in this research in the following: i) provide a framework for
detecting and classifying breast cancer tumor that use CNN architectures, ii) apply the transfer learning
concept and provide a comparative analysis of accuracy for three different deep CNN architectures, and iii)
using a combination of extracted features from various networks to improve classification accuracy.
2. PROPOSED METHOD
In this paper, we suggest a framework by using three different deep CNN architectures: VGG16,
ResNet50, and Inception, these CNNs were pre-trained on ImageNet dataset [17]. We used them for the
breast cancer tumor detection and classification in histopathology images. The suggested model combined
various low-level features that were separately extracted from various CNN architectures, and then fed it into
a fully connected (FC) layer to classify the benign and malignant tumor.
2.1. Pre-trained CNN architectures for features extraction
In this section, three different CNN models are used for feature extraction of the proposed method,
VGG-16 [18], ResNet50 [19], and Inception-v3 [20]. These models are concatenated into the FC layer which
is used to classify breast cancer tumor. The ImageNet dataset, which contains multiple generic image
descriptors, was used to pre-train these CNNs [21], and then feature extraction is performed using transfer
learning concept. The structures for each CNN architectures are described briefly:
2.1.1. VGG-16 architecture
VGG-16 is made up of 16 layers, containing 13 convolution layers, pooling layers, and three FC
layers [18]. The number of channels in convolution layers is 64 channels in the first layer and rises after each
pooling layer by a factor of two until it reaches 512. A 3x3 window size filter and a 2x2 pooling network are
used in the convolution network. VGG-16 is a convolutional network similar to the model AlexNet but
contains more convolution layers. Because of its simple architecture, it outperforms AlexNet. VGG-16’s
basic architecture is shown in Figure 1.
2.1.2. ResNet50 architecture
Residual networks (ResNet) [19] are a group of deep neural networks that have architectures that are
similar but varying depths that perform well at classification tasks on ImageNet [22]. To deal with the
degradation problem of deep neural networks, the residual learning unit is a structure introduced by ResNet
[19]. The merit of this structure is it enhances classification accuracy without raising model complexity.
ResNet50’s basic architecture is depicted in Figure 2.
2.1.3. Inception-v3 architecture
Inception-v3 [20] is an enhanced version of the GoogLeNet architecture [23], which uses transfer
learning in biomedical applications to achieve high classification performance [24], [25]. Inception suggested
a model that combines many convolutional filters of varying sizes into a single one. As a result of this design,
3. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 12, No. 6, December 2022: 6736-6743
6738
the computational complexity and the number of trained parameters are reduced. Inception-v3’s basic
architecture is depicted in Figure 3.
Figure 1. The VGG-16 CNN architecture [18]
Figure 2. The ResNet50 CNN architecture [19]
Figure 3. Inception-v3 CNN architecture [20]
2.2. Data augmentation
Because CNN’s performance weakens when used with small datasets due to overfitting [26], which
gives unwell results on test data despite it achieving good performance in the training data, it requires large
data sets to attain higher accuracy. In this paper, to reduce overfitting issues and expand the dataset, a data
augmentation technique is used [26]. The number of data at training is increased by using image processing
methods to apply geometric transformations to image datasets at data augmentation technique. During the
training stage, using flipping samples vertical and horizontal, scaling, translation, and rotation, the training
data is increased. Because microscopic images rotationally are invariant, cancer tumor microscopic images
can be easily analyzed from various positions without affecting the diagnosis [27].
Input
3x3 Conv, 64
3x3 Conv, 64
2x2 Pooling
3x3 Conv, 128
3x3 Conv, 128
2x2 Pooling
3x3 Conv, 256
3x3 Conv, 256
3x3 Conv, 256
2x2 Pooling
3x3 Conv, 512
3x3 Conv, 512
3x3 Conv, 512
2x2 Pooling
3x3 Conv, 512
3x3 Conv, 512
3x3 Conv, 512
2x2 Pooling
FC - 4096
FC - 4096
FC - 1000
Output
Conv1
Patch: 7x7
Stride: 2
3 x Conv2_x
Conv: 1x1, 64
Conv: 3x3, 64
Conv: 1x1, 256
Pool
Patch: 3x3
Stride: 2
4 x Conv3_x
Conv: 1x1, 128
Conv: 3x3, 128
Conv: 1x1, 512
6 x Conv4_x
Conv: 1x1, 256
Conv: 3x3, 256
Conv: 1x1, 1024
3 x Conv5_x
Conv: 1x1, 512
Conv: 3x3, 512
Conv: 1x1, 2048
Conv
Patch: 3x3
Stride: 2
Conv padded
Patch: 3x3
Stride: 1
Conv
Patch: 3x3
Stride: 1
Pool
Patch: 3x3
Stride: 2
Conv
Patch: 3x3
Stride: 1
Conv
Patch: 3x3
Stride: 2
Conv
Patch: 3x3
Stride: 1
3 x Inception
Model 1
5 x Inception
Model 2
2 x Inception
Model 3
Pool
Patch: 8x8
Stride: 0
Linear
Logits
4. Int J Elec & Comp Eng ISSN: 2088-8708
Deep learning for cancer tumor classification using transfer learning and … (Abdallah Mohamed Hassan)
6739
2.3. Transfer learning
To achieve high accuracy and train a model from scratch, it needs a large amount of data, but getting
a large dataset of relevant problems can be difficult in some cases. As a result, the term “transfer learning”
has been introduced. The CNN model structure is first trained for a task using a large image dataset related to
that task and then transferred to a wanted task which is trained on a small dataset [28].
The similarity between the source training dataset and the target dataset and selection of pre-trained
model are two steps in the process of transfer learning process. If the size of the used dataset is small and
related to the original training dataset, there is high overfitting probability. If the target dataset size is large
and different from the source training dataset, there is low overfitting probability [16], and in this case all that
is required for the pre-trained model is fine-tuning.
2.4. The proposed network structure
First, the three CNN architectures VGG16, ResNet50, and Inception-v3 are trained on a dataset of
general images from 1,000 categories using ImageNet dataset [17], after which a transfer learning method
can be used, allowing CNN architectures to learn generic characteristics from other image datasets without
the need to train models from scratch. The CNN model’s transfer learning architecture is shown in Figure 4,
the pre-trained network acts as a features extractor for general features of image, and add FC layers for
classification. The details of the extracted features from the CNN architecture can be summarized as the
following: i) VGG-16: 512 feature is extracted from the last layer as shown in Figure 1; ii) ResNet50: 2048
feature is extracted from the last layer as shown in Figure 2; iii) Inception-v3: 2048 feature is extracted from
the last logits layer as shown in Figure 3.
The extracted features from the per-trained models are concatenated to form 4,612-dimensional
feature. The concatenated features are then fed into the FC layer using average pooling for classification of
the benign and malignant tumor. Figure 5 shows the structure of the proposed feature concatenation scheme.
Figure 4. The CNN model’s transfer learning architecture
Figure 5. The proposed feature concatenation structure
3. EXPERIMENTAL RESULTS AND DISCUSSION
3.1. Dataset description
The proposed framework is evaluated using the breast cancer histopathology images (BreakHis)
dataset [29]. The dataset consists of 7,909 breast cancer histopathology images using various factors of
magnification (40X, 100X, 200X, and 400X) which were collected from patients and contain
5,429 malignant and 2,480 benign samples. The details of the dataset used are illustrated in Table 1.
Table 1. The breast cancer histopathological images dataset
Magnification Malignant Benign Total
40X 1,370 625 1,995
100X 1,437 644 2,081
200X 1,390 623 2,013
400X 1,232 588 1,820
Total 5,429 2480 7,909
Image FC layers
Pre-trained CNN
model
Output
Image
Features
concatenation
Pre-trained
VGG-16
FC layers
Pre-trained
ResNet50
Pre-trained
Inception-v3
Output
5. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 12, No. 6, December 2022: 6736-6743
6740
3.2. Results and discussion
We split the dataset into two parts: the first one contains 80% of the dataset as a training set
(6,328 image) for training the models and the other contains 20% as a testing set (1,581 image) for testing the
models. The accuracy of classification for the proposed architecture is compared to three single transfer
learning network architectures: VGG16, ResNet50, and Inception individually. Table 2 displays the
classification accuracy results obtained by the proposed model and other used architectures in different
magnification factor and then the average magnification accuracy is calculated for each model.
As the results shown in Table 2, it can be noticed that the proposed framework achieved the highest
accuracy of 99.732%, 98.947%, 99.328%, and 98.626% at magnification factor 40X, 100X, 200X, and 400X
respectively. The VGG-16, ResNet50, and Inception-v3 architectures give an average magnification accuracy
of 95.25%, 90.19%, and 97.02%, respectively. Meanwhile the suggested model achieves 99.16% as an
average accuracy. The results shown indicate that the suggested architecture outperforms the three single
architectures and achieves high accuracy in the cancer tumor classification.
Table 2. The accuracy of the proposed model and other CNN models based on different magnification factor
CNN model Magnification accuracy (%) Average magnification accuracy (%)
40X 100X 200X 400X
VGG-16 96.242 96.447 96.102 92.214 95.25
ResNet50 89.262 92.368 88.441 90.687 90.19
Inception-v3 97.572 96.586 97.172 96.748 97.02
Proposed Framework 99.732 98.947 99.328 98.626 99.16
In this situation, to get more accurate results for the different models, the entire dataset is divided
using multiple splitting ratio procedures into training and testing parts, such as 90-10%, 80-20%, and 70-30%
ratios. The 90-10% splitting ratio indicates that 90% of the data are used when train the model, while the
remaining 10% are used to test the model. Table 3 and Figure 6 compare the proposed framework
architecture to the other CNN models based on different splitting ratios. The "Class Type" represents the type
of tumor, where B denotes benign and M is malignant cancer in Table 3. It shows the precision of each class
type and the accuracy of each splitting ratio. In addition, it provides the average accuracy based on splitting
ratios of each CNN models. Figure 6 shows the accuracy of CNN architectures at different splitting ratios and
an average accuracy for the architectures. As shown in Table 3 and Figure 6, the proposed framework
architecture achieves the highest accuracy compared to single architectures in the classification of the cancer
tumor. The VGG-16, ResNet50, and Inception-v3 architectures achieve an average accuracy 95.68%,
88.43%, and 96.49% respectively, while the suggested model achieves an average accuracy 98.76%.
Table 3. Comparative analysis of accuracy based on different splitting ratios for proposed model with other
CNN models
CNN model Splitting ratio Class type Precision Accuracy of splitting ratio (%) Average accuracy (%)
VGG-16 90%-10% B
M
95.05
96.73
96.20
80%-20% B
M
93.72
96.22
95.44 95.68
70%-30% B
M
95.82
95.20
95.40
ResNet50 90%-10% B
M
84.55
93.35
90.60
80%-20% B
M
72.87
95.20
88.22 88.43
70%-30% B
M
79.92
89.49
86.49
Inception-v3 90%-10% B
M
98.37
96.31
96.95
80%-20% B
M
94.94
96.96
96.32 96.49
70%-30% B
M
97.77
95.48
96.2
Proposed Framework 90%-10% B
M
97.96
99.81
99.23
80%-20% B
M
97.58
99.17
98.67 98.76
70%-30% B
M
97.98
98.58
98.39
6. Int J Elec & Comp Eng ISSN: 2088-8708
Deep learning for cancer tumor classification using transfer learning and … (Abdallah Mohamed Hassan)
6741
Figure 6. Comparative analysis of accuracy for CNN models
3.3. Comparison between the proposed framework and other methods
In this section, it is preferable to compare the results achieved from our proposed framework
technique with those achieved using various conventional classification methods as indicated in Table 4. It
can be indicated that the performed structures in [13]–[16] have an accuracy 92.63%, 97%, 97.08%, and
97.52% respectively, while our proposed scenario has the highest accuracy of all four procedures at 98.76%.
These results demonstrate the suggested framework's superiority over other similar methodologies.
Table 4. Comparison between the proposed framework and other methods
Method Accuracy (%)
Nguyen [13] 92.63
Kensert [14] 97.00
Vesal [15] 97.08
Khan [16] 97.52
Proposed Framework 98.76
4. CONCLUSION
In this study, we suggest a deep learning framework based on the transfer learning principle for
detection and classification of breast cancer Histopathological images. In this framework, by using three
different deep CNN models (VGG-16, ResNet50, and Inception-v3), the features from breast cancer images
are extracted, and then concatenated to improve classification accuracy. Data augmentation is a method for
increasing the dataset size to minimize over-fitting issues and improve the efficiency of CNN architecture.
The work presented here shows how transfer learning and features concatenation of multiple CNN
architectures can improve classification accuracy when compared to single CNN networks and achieves
excellent classification accuracy. It is also compared the proposed framework's performance to that of
different existing classification methods, and it is found that the proposed model achieves 98.76% as an
average accuracy.
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BIOGRAPHIES OF AUTHORS
Abdallah Mohamed Hassan received the B.Sc. and M.Sc. degrees in Electronics
and Communications Engineering from the Faculty of Engineering, Al-Azhar University,
Cairo, Egypt, in 2012 and 2018 respectively. He is an Assistant Lecturer in the Faculty of
Engineering, Al-Azhar University, Cairo, Egypt. He is currently a Ph.D. student at Faculty of
Engineering, Al-Azhar university, Cairo. His research activities are within artificial intelligent
and deep learning. He can be contacted at email: abdallah.mohamed@azhar.edu.eg.
8. Int J Elec & Comp Eng ISSN: 2088-8708
Deep learning for cancer tumor classification using transfer learning and … (Abdallah Mohamed Hassan)
6743
Mohamed Bakry El-Mashade received the B.Sc. degree in electrical
engineering from Al-Azhar University, Cairo, in 1978, the M.Sc. degree in the theory of
communications from Cairo University, in 1982, Le D.E.A. d’Electronique (Spécialité:
Traitment du Signal), and Le Diploma de Doctorat (Spécialité: Composants, Signaux et
Systems) in optical communications, from USTL, L’Academie de Montpellier, Montpellier,
France, in 1985 and 1987 respectively. He serves on the Editorial Board of several
International Journals. He has also served as a reviewer for many international journals. He
was the author of more than 60 peer-reviewed journal articles and the coauthor of more than
60 journal technical papers as well as three international book chapters. He serves on the
Editorial Board of International Journal of Communications, Networks and System Sciences
IJCNS. He has organized a special issue on Recent Trends of Wireless Communication
Networks for International Journal of Communications, Network and Systems sciences
IJCNS. He received the best research paper award from International Journal of
Semiconductor Science and Technology in 2014 for his work on “Noise Modeling Circuit of
Quantum Structure Type of Infrared Photodetectors”. He won the Egyptian Encouraging
Award, in Engineering Science, two times (1998 and 2004). He was included in the American
Society ‘Marquis Who’s Who’ as a ‘Distinguishable Scientist’ in 2004, and in the
International Biographkal Centre of Cambridge, England as an ‘Outstanding Scientist’ in
2005. He has been named an official listed in the 2020 edition of ‘Marquis Who’s Who in the
World®’
. His research interests include statistical signal processing, digital and optical signal
processing, free space optical communications, fiber Bragg grating, quantum structure family
of optical devices, SDR, cognitive radio, and software defined radar and SAR. He can be
contacted at email: elmashade@yahoo.com.
Ashraf Aboshosha received the B.Sc. in industrial electronics from Menoufia
University, Egypt in 1990. Since 1992 he is a researcher at the (NCRRT/EAEA), where he
served as a junior member of the instrumentation and control committee. In 1997 he received
his M.Sc. in automatic control and measurement engineering. From 1997 to 1998 he was guest
researcher at research centre Jülich (FZJ), Germany. From 2000 to 2004 he was a doctoral
student (DAAD-scholarship) at Wilhelm Schickard Institute for informatics (WSI), Eberhard-
Karls-University, Tübingen, Germany. Where he received his Doctoral degree (Dr. rer. nat.) in
2004. He is the E-i-C of ICGST LLC, Delaware, USA. He is the author of more than 7 Books
(4 in English and 3 in Arabic) and about 50 academic articles. He supervised more than 20
academic M.Sc. & Ph.D. theses. He can be contacted at email: editor@icgdt.com.