This document proposes developing a generalized deep learning model to classify gastric, colon, and renal cancer using a single model. The model would be trained on whole slide images of tissue samples fed through an EfficientNet model pre-trained on ImageNet. The model would be trained using transfer learning with partial transfusion to demonstrate the ability to classify pathology images from different sites. Previous studies have developed models to classify individual tissue types but not a unified model. The proposed model aims to address situations where the tissue site of origin is unknown.
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.
Breast cancer histological images nuclei segmentation and optimized classifi...IJECEIAES
Breast cancer incidences have grown worldwide during the previous few years. The histological images obtained from a biopsy of breast tissues are regarded as being the highest accurate approach to determine whether any cells exhibit symptoms of cancer. The visible position of nuclei inside the image is achieved through the use of instance segmentation, nevertheless, this work involves nucleus segmentation and features classification of the predicted nucleus for the achievement of best accuracy. The extracted features map using the feature pyramid network has been modified using segmenting objects by locations (SOLO) convolution with grasshopper optimization for multiclass classification. A breast cancer multiclassification technique based on a suggested deep learning algorithm was examined to achieve the accuracy of 99.2% using a huge database of ICIAR 2018, demonstrating the method’s efficacy in offering an important weapon for breast cancer multi-classification in a medical setting. The segmentation accuracy achieved is 88.46%.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
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.
Breast cancer histological images nuclei segmentation and optimized classifi...IJECEIAES
Breast cancer incidences have grown worldwide during the previous few years. The histological images obtained from a biopsy of breast tissues are regarded as being the highest accurate approach to determine whether any cells exhibit symptoms of cancer. The visible position of nuclei inside the image is achieved through the use of instance segmentation, nevertheless, this work involves nucleus segmentation and features classification of the predicted nucleus for the achievement of best accuracy. The extracted features map using the feature pyramid network has been modified using segmenting objects by locations (SOLO) convolution with grasshopper optimization for multiclass classification. A breast cancer multiclassification technique based on a suggested deep learning algorithm was examined to achieve the accuracy of 99.2% using a huge database of ICIAR 2018, demonstrating the method’s efficacy in offering an important weapon for breast cancer multi-classification in a medical setting. The segmentation accuracy achieved is 88.46%.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Cervical cancer diagnosis based on cytology pap smear image classification us...TELKOMNIKA JOURNAL
Doctors and pathologists have long been concerned about determining the malignancy from cell images. This task is laborious, time-consuming and needs expertise. Due to this reason, automated systems assist pathologists in providing a second opinion to arrive at accurate decision based on cytology images. The classification of cytology images has always been a difficult challenge among the various image analysis approaches due to its extreme intricacy. The thrust for early diagnosis of cervical cancer has always fuelled the research in medical image analysis for cancer detection. In this paper,
an investigative study for the classification of cytology images is proposed.
The proposed study uses the discrete coefficient transform (DCT) coefficient and Haar transform coefficients as features. These features are given as a input to seven different machine learning algorithms for normal and abnormal pap smear images classification. In order to optimize the feature size, fractional coefficients are used to form the five different sizes of feature vectors. In the proposed work, DCT transform has given the highest classification accuracy of 81.11%. Comparing the different machine learning algorithms the overall best performance is given by the random forest classifier.
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.
deep learning applications in medical image analysis brain tumorVenkat Projects
The tremendous success of machine learning algorithms at image recognition tasks in recent years intersects with a time of dramatically increased use of electronic medical records and diagnostic imaging. This review introduces the machine learning algorithms as applied to medical image analysis, focusing on convolutional neural networks, and emphasizing clinical aspects of the _eld. The advantage of machine learning in an era of medical big data is that signi_cant hierarchal relationships within the data can be discovered algorithmically without laborious hand-crafting of features. We cover key research areas and applications of medical image classi_cation, localization, detection, segmentation, and registration. We conclude by discussing research obstacles, emerging trends, and possible future directions.
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.
Ultrasound image segmentation through deep learning based improvised U-Netnooriasukmaningtyas
Thyroid nodule are fluid or solid lump that are formed within human’s gland and most thyroid nodule doesn’t show any symptom or any sign; moreover there are certain percentage of thyroid gland are cancerous and which could lead human into critical situation up to death. Hence, it is one of the important type of cancer and also it is important for detection of cancer. Ultrasound imaging is widely popular and frequently used tool for diagnosing thyroid cancer, however considering the wide application in clinical area such estimating size, shape and position of thyroid cancer. Further, it is important to design automatic and absolute segmentation for better detection and efficient diagnosis based on US-image. Segmentation of thyroid gland from the ultrasound image is quiet challenging task due to inhomogeneous structure and similar existence of intestine. Thyroid nodule can appear anywhere and have any kind of contrast, shape and size, hence segmentation process needs to designed carefully; several researcher have worked in designing the segmentation mechanism , however most of them were either semi-automatic or lack with performance metric, however it was suggested that U-Net possesses great accuracy. Hence, in this paper, we proposed improvised U-Net which focuses on shortcoming of U-Net, the main aim of this research work is to find the probable Region of interest and segment further. Furthermore, we develop High level and low-level feature map to avoid the low-resolution problem and information; later we develop dropout layer for further optimization. Moreover proposed model is evaluated considering the important metrics such as accuracy, Dice Coefficient, AUC, F1-measure and true positive; our proposed model performs better than the existing model.
Class imbalance is a pervasive issue in the field of disease classification from
medical images. It is necessary to balance out the class distribution while training a model. However, in the case of rare medical diseases, images from affected
patients are much harder to come by compared to images from non-affected
patients, resulting in unwanted class imbalance. Various processes of tackling
class imbalance issues have been explored so far, each having its fair share of
drawbacks. In this research, we propose an outlier detection based image classification technique which can handle even the most extreme case of class imbalance. We have utilized a dataset of malaria parasitized and uninfected cells. An
autoencoder model titled AnoMalNet is trained with only the uninfected cell images at the beginning and then used to classify both the affected and non-affected
cell images by thresholding a loss value. We have achieved an accuracy, precision, recall, and F1 score of 98.49%, 97.07%, 100%, and 98.52% respectively,
performing better than large deep learning models and other published works.
As our proposed approach can provide competitive results without needing the
disease-positive samples during training, it should prove to be useful in binary
disease classification on imbalanced datasets.
Titles with Abstracts_2023-2024_Digital Image processing.pdfinfo751436
Digital image processing (DIP) refers to the manipulation of digital images using various algorithms and techniques to enhance or extract information from the images. There are several advantages to using digital image processing:
Enhancement of Image Quality:
DIP allows for the improvement of image quality by reducing noise, correcting distortions, and enhancing details. This is particularly useful in medical imaging, satellite imagery, and surveillance.
Image Restoration:
It helps in restoring images that have been degraded due to factors such as noise, blurring, or compression. Restoration techniques can improve the visual quality of images.
Image Compression:
DIP plays a crucial role in image compression, allowing for the reduction of file sizes while maintaining an acceptable level of image quality. This is essential for efficient storage and transmission of images over networks.
Image Recognition and Computer Vision:
DIP is widely used in computer vision applications for tasks such as object recognition, face detection, and gesture recognition. It enables machines to interpret and understand visual information.
Medical Image Processing:
In medical imaging, DIP is used for tasks like tumor detection, organ segmentation, and image reconstruction. It assists healthcare professionals in diagnosis and treatment planning.
Remote Sensing:
In satellite imagery and remote sensing applications, DIP helps analyze and interpret data for various purposes, including environmental monitoring, agriculture, and disaster management.
Geographic Information Systems (GIS):
DIP is employed in GIS to process and analyze spatial data, enabling the extraction of meaningful information from satellite imagery and maps.
Video Processing:
DIP is used in video processing for tasks such as video compression, object tracking, and motion analysis. It is essential in surveillance systems and video editing.
Authentication and Security:
DIP is utilized in authentication systems, such as fingerprint recognition and iris scanning. It also plays a role in security applications, such as facial recognition for access control.
Automated Inspection and Quality Control:
In industrial settings, DIP is used for automated inspection and quality control. It helps identify defects and ensures the production of high-quality products.
Entertainment and Multimedia:
DIP is integral in various entertainment and multimedia applications, including image and video editing, special effects, and virtual reality.
Scientific Research:
Researchers use DIP in fields such as astronomy, biology, and physics for image analysis, data extraction, and visualization.
In summary, digital image processing offers a wide range of advantages across various fields, contributing to improvements in image quality, information extraction, and automated decision-making processes.
Human health is the real wealth for a society. Consequently prevention of health from complex diseases like cancer needs the diagnosis of these entire viruses at an early stage. Colon cancer, the most common one, reached the highest rate among all the other types recently. Colorectal cancer gets developed either in colon or in the rectum inside the large intestine, due to the abnormal growth of the cells. Computer-aided decision support system has become one of the major research topics in medical imaging field during the past two decades to detect cancers. Detecting and screening of colorectal cancers are done by a Computed Tomography. The implemented algorithm determines the locations and features of glands which are affected by cancer tissues and save this
information for the subsequent diagnosis. The proposed algorithm carries out the diagnosis with two modules:
One known as the gland detection and the other one referred as the nuclei detection. Gland detection is performed in the proposed algorithm using color segmentation either through HSV or LAB transformation. Noise removal and erosion of the input image is performed for enhancing the selection of the affected tissues. The boundary detection and connection is established through Markov Chain model to identify the affected tissues with proper threshold. The first module detects the glands where the possibly of miss detection is more. Hence to remove the miss detected glands the algorithm proceed for the second module referred as nuclei detection. The most well
known region growing methodology is slightly modified to increase the speed and reduce the memory size To provide the execution in low-end clients, the whole image is cracked into smaller tiles and after the processing of each individual tiles , the results are to be merged to get back the original size. After nuclei detection if the number of nucleus is more that glands are miss detected glands and they are removed.
Deep learning for cancer tumor classification using transfer learning and fe...IJECEIAES
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%.
Gaussian Multi-Scale Feature Disassociation Screening in Tuberculosiseijceronline
Tuberculosis is a major health threat in many regions of the world. When left undiagnosed and consequently untreated, death rates of patients with tuberculosis are high. We first extract the lung region using a lung nodule Edge detection method. For this lung region, we compute a set of texture and shape features, which enable the x-rays to be classified as normal or abnormal using a binary classifier. Thus, a development of edge detection solution to address these requirements can be implemented in a wide range of situations. The general criteria for edge detection includes detection of edge with lower rorrate ,whichmeans that the detection should accurately catch as many edges.
The current big challenge facing radiologists in healthcare is the automatic detection and classification of masses in breast mammogram images. In the last few years, many researchers have proposed various solutions to this problem. These solutions are effectively dependent and work on annotated breast image data. But these solutions fail when applied to unlabeled and non-annotated breast image data. Therefore, this paper provides the solution to this problem with the help of a neural network that considers any kind of unlabeled data for its procedure. In this solution, the algorithm automatically extracts tumors in images using a segmentation approach, and after that, the features of the tumor are extracted for further processing. This approach used a double thresholding-based segmentation technique to obtain a perfect location of the tumor region, which was not possible in existing techniques in the literature. The experimental results also show that the proposed algorithm provides better accuracy compared to the accuracy of existing algorithms in the literature.
Cervical cancer diagnosis based on cytology pap smear image classification us...TELKOMNIKA JOURNAL
Doctors and pathologists have long been concerned about determining the malignancy from cell images. This task is laborious, time-consuming and needs expertise. Due to this reason, automated systems assist pathologists in providing a second opinion to arrive at accurate decision based on cytology images. The classification of cytology images has always been a difficult challenge among the various image analysis approaches due to its extreme intricacy. The thrust for early diagnosis of cervical cancer has always fuelled the research in medical image analysis for cancer detection. In this paper,
an investigative study for the classification of cytology images is proposed.
The proposed study uses the discrete coefficient transform (DCT) coefficient and Haar transform coefficients as features. These features are given as a input to seven different machine learning algorithms for normal and abnormal pap smear images classification. In order to optimize the feature size, fractional coefficients are used to form the five different sizes of feature vectors. In the proposed work, DCT transform has given the highest classification accuracy of 81.11%. Comparing the different machine learning algorithms the overall best performance is given by the random forest classifier.
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.
deep learning applications in medical image analysis brain tumorVenkat Projects
The tremendous success of machine learning algorithms at image recognition tasks in recent years intersects with a time of dramatically increased use of electronic medical records and diagnostic imaging. This review introduces the machine learning algorithms as applied to medical image analysis, focusing on convolutional neural networks, and emphasizing clinical aspects of the _eld. The advantage of machine learning in an era of medical big data is that signi_cant hierarchal relationships within the data can be discovered algorithmically without laborious hand-crafting of features. We cover key research areas and applications of medical image classi_cation, localization, detection, segmentation, and registration. We conclude by discussing research obstacles, emerging trends, and possible future directions.
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.
Ultrasound image segmentation through deep learning based improvised U-Netnooriasukmaningtyas
Thyroid nodule are fluid or solid lump that are formed within human’s gland and most thyroid nodule doesn’t show any symptom or any sign; moreover there are certain percentage of thyroid gland are cancerous and which could lead human into critical situation up to death. Hence, it is one of the important type of cancer and also it is important for detection of cancer. Ultrasound imaging is widely popular and frequently used tool for diagnosing thyroid cancer, however considering the wide application in clinical area such estimating size, shape and position of thyroid cancer. Further, it is important to design automatic and absolute segmentation for better detection and efficient diagnosis based on US-image. Segmentation of thyroid gland from the ultrasound image is quiet challenging task due to inhomogeneous structure and similar existence of intestine. Thyroid nodule can appear anywhere and have any kind of contrast, shape and size, hence segmentation process needs to designed carefully; several researcher have worked in designing the segmentation mechanism , however most of them were either semi-automatic or lack with performance metric, however it was suggested that U-Net possesses great accuracy. Hence, in this paper, we proposed improvised U-Net which focuses on shortcoming of U-Net, the main aim of this research work is to find the probable Region of interest and segment further. Furthermore, we develop High level and low-level feature map to avoid the low-resolution problem and information; later we develop dropout layer for further optimization. Moreover proposed model is evaluated considering the important metrics such as accuracy, Dice Coefficient, AUC, F1-measure and true positive; our proposed model performs better than the existing model.
Class imbalance is a pervasive issue in the field of disease classification from
medical images. It is necessary to balance out the class distribution while training a model. However, in the case of rare medical diseases, images from affected
patients are much harder to come by compared to images from non-affected
patients, resulting in unwanted class imbalance. Various processes of tackling
class imbalance issues have been explored so far, each having its fair share of
drawbacks. In this research, we propose an outlier detection based image classification technique which can handle even the most extreme case of class imbalance. We have utilized a dataset of malaria parasitized and uninfected cells. An
autoencoder model titled AnoMalNet is trained with only the uninfected cell images at the beginning and then used to classify both the affected and non-affected
cell images by thresholding a loss value. We have achieved an accuracy, precision, recall, and F1 score of 98.49%, 97.07%, 100%, and 98.52% respectively,
performing better than large deep learning models and other published works.
As our proposed approach can provide competitive results without needing the
disease-positive samples during training, it should prove to be useful in binary
disease classification on imbalanced datasets.
Titles with Abstracts_2023-2024_Digital Image processing.pdfinfo751436
Digital image processing (DIP) refers to the manipulation of digital images using various algorithms and techniques to enhance or extract information from the images. There are several advantages to using digital image processing:
Enhancement of Image Quality:
DIP allows for the improvement of image quality by reducing noise, correcting distortions, and enhancing details. This is particularly useful in medical imaging, satellite imagery, and surveillance.
Image Restoration:
It helps in restoring images that have been degraded due to factors such as noise, blurring, or compression. Restoration techniques can improve the visual quality of images.
Image Compression:
DIP plays a crucial role in image compression, allowing for the reduction of file sizes while maintaining an acceptable level of image quality. This is essential for efficient storage and transmission of images over networks.
Image Recognition and Computer Vision:
DIP is widely used in computer vision applications for tasks such as object recognition, face detection, and gesture recognition. It enables machines to interpret and understand visual information.
Medical Image Processing:
In medical imaging, DIP is used for tasks like tumor detection, organ segmentation, and image reconstruction. It assists healthcare professionals in diagnosis and treatment planning.
Remote Sensing:
In satellite imagery and remote sensing applications, DIP helps analyze and interpret data for various purposes, including environmental monitoring, agriculture, and disaster management.
Geographic Information Systems (GIS):
DIP is employed in GIS to process and analyze spatial data, enabling the extraction of meaningful information from satellite imagery and maps.
Video Processing:
DIP is used in video processing for tasks such as video compression, object tracking, and motion analysis. It is essential in surveillance systems and video editing.
Authentication and Security:
DIP is utilized in authentication systems, such as fingerprint recognition and iris scanning. It also plays a role in security applications, such as facial recognition for access control.
Automated Inspection and Quality Control:
In industrial settings, DIP is used for automated inspection and quality control. It helps identify defects and ensures the production of high-quality products.
Entertainment and Multimedia:
DIP is integral in various entertainment and multimedia applications, including image and video editing, special effects, and virtual reality.
Scientific Research:
Researchers use DIP in fields such as astronomy, biology, and physics for image analysis, data extraction, and visualization.
In summary, digital image processing offers a wide range of advantages across various fields, contributing to improvements in image quality, information extraction, and automated decision-making processes.
Human health is the real wealth for a society. Consequently prevention of health from complex diseases like cancer needs the diagnosis of these entire viruses at an early stage. Colon cancer, the most common one, reached the highest rate among all the other types recently. Colorectal cancer gets developed either in colon or in the rectum inside the large intestine, due to the abnormal growth of the cells. Computer-aided decision support system has become one of the major research topics in medical imaging field during the past two decades to detect cancers. Detecting and screening of colorectal cancers are done by a Computed Tomography. The implemented algorithm determines the locations and features of glands which are affected by cancer tissues and save this
information for the subsequent diagnosis. The proposed algorithm carries out the diagnosis with two modules:
One known as the gland detection and the other one referred as the nuclei detection. Gland detection is performed in the proposed algorithm using color segmentation either through HSV or LAB transformation. Noise removal and erosion of the input image is performed for enhancing the selection of the affected tissues. The boundary detection and connection is established through Markov Chain model to identify the affected tissues with proper threshold. The first module detects the glands where the possibly of miss detection is more. Hence to remove the miss detected glands the algorithm proceed for the second module referred as nuclei detection. The most well
known region growing methodology is slightly modified to increase the speed and reduce the memory size To provide the execution in low-end clients, the whole image is cracked into smaller tiles and after the processing of each individual tiles , the results are to be merged to get back the original size. After nuclei detection if the number of nucleus is more that glands are miss detected glands and they are removed.
Deep learning for cancer tumor classification using transfer learning and fe...IJECEIAES
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%.
Gaussian Multi-Scale Feature Disassociation Screening in Tuberculosiseijceronline
Tuberculosis is a major health threat in many regions of the world. When left undiagnosed and consequently untreated, death rates of patients with tuberculosis are high. We first extract the lung region using a lung nodule Edge detection method. For this lung region, we compute a set of texture and shape features, which enable the x-rays to be classified as normal or abnormal using a binary classifier. Thus, a development of edge detection solution to address these requirements can be implemented in a wide range of situations. The general criteria for edge detection includes detection of edge with lower rorrate ,whichmeans that the detection should accurately catch as many edges.
The current big challenge facing radiologists in healthcare is the automatic detection and classification of masses in breast mammogram images. In the last few years, many researchers have proposed various solutions to this problem. These solutions are effectively dependent and work on annotated breast image data. But these solutions fail when applied to unlabeled and non-annotated breast image data. Therefore, this paper provides the solution to this problem with the help of a neural network that considers any kind of unlabeled data for its procedure. In this solution, the algorithm automatically extracts tumors in images using a segmentation approach, and after that, the features of the tumor are extracted for further processing. This approach used a double thresholding-based segmentation technique to obtain a perfect location of the tumor region, which was not possible in existing techniques in the literature. The experimental results also show that the proposed algorithm provides better accuracy compared to the accuracy of existing algorithms in the literature.
Similar to Generalized deep learning model for Classification of Gastric, Colon and Renal Cancer (20)
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
6th International Conference on Machine Learning & Applications (CMLA 2024)ClaraZara1
6th International Conference on Machine Learning & Applications (CMLA 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of on Machine Learning & Applications.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.