Localization of the cancerous region as well as classification of the type of the cancer is highly inter-linked with each other. However, investigation towards existing approaches depicts that these problems are always iindividually solved where there is still a big research gap for a generalized solution towards addressing both the problems. Therefore, the proposed manuscript presents a simple, novel, and less-iterative computational model that jointly address the localization-classification problems taking the case study of early diagnosis of breast cancer. The proposed study harnesses the potential of simple bio-inspired optimization technique in order to obtained better local and global best outcome to confirm the accuracy of the outcome. The study outcome of the proposed system exhibits that proposed system offers higher accuracy and lower response time in contrast with other existing classifiers that are freqently witnessed in existing approaches of classification in medical image process.
Thyroid Cancer Detection for Myanmar Review and Recommendationijtsrd
Thyroid cancer is common cancer nowadays in most of the people irrespective of country, gender and regions, and so as in Myanmar. This paper brings significant work on thyroid cancer detection using image processing and segmentation methods so that appropriate work can be carried out for the benefit of people of Myanmar. The critical study of important papers gives challenges in existing work on cancer detection using several methods but there is not robust method for thyroid cancer detection. So, we attempt to suggest a robust set of methods for addressing this problem for Myanmar since there is very limited work on detection of cancer in the country. Hanni Htun | Moe Moe Htay | Sin Thi Yar Myint | Phyo Hay Mar Wai | Pyae Pyae Soe "Thyroid Cancer Detection for Myanmar: Review and Recommendation" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26723.pdfPaper URL: https://www.ijtsrd.com/computer-science/artificial-intelligence/26723/thyroid-cancer-detection-for-myanmar-review-and-recommendation/hanni-htun
Framework for progressive segmentation of chest radiograph for efficient diag...IJECEIAES
Segmentation is one of the most essential steps required to identify the inert object in the chest x-ray. A review with the existing segmentation techniques towards chest x-ray as well as other vital organs was performed. The main objective was to find whether existing system offers accuracy at the cost of recursive and complex operations. The proposed system contributes to introduce a framework that can offer a good balance between computational performance and segmentation performance. Given an input of chest x-ray, the system offers progressive search for similar image on the basis of similarity score with queried image. Region-based shape descriptor is applied for extracting the feature exclusively for identifying the lung region from the thoracic region followed by contour adjustment. The final segmentation outcome shows accurate identification followed by segmentation of apical and costophrenic region of lung. Comparative analysis proved that proposed system offers better segmentation performance in contrast to existing system.
ENHANCING SEGMENTATION APPROACHES FROM GAUSSIAN MIXTURE MODEL AND EXPECTED MA...Christo Ananth
Automatic liver tumor segmentation would bigly influence liver treatment organizing strategy and follow-up assessment, as a result of organization and joining of full picture information. Right now, develop a totally programmed technique for liver tumor division in CT picture. Introductory liver division comprises of applying a functioning form strategy. In the wake of separating liver applying Super pixel division Algorithm for portioning liver tumor proficiently. In the proposed work, we will investigate these procedures so as to improve division of various segments of the CT pictures. The exploratory outcomes indicated that the proposed strategy was exact for liver tumor division.
A New Approach to the Detection of Mammogram Boundary IJECEIAES
Mammography is a method used for the detection of breast cancer. computer-aided diagnostic (CAD) systems help the radiologist in the detection and interpretation of mass in breast mammography. One of the important information of a mass is its contour and its form because it provides valuable information about the abnormality of a mass. The accuracy in the recognition of the shape of a mass is related to the accuracy of the detected mass contours. In this work we propose a new approach for detecting the boundaries of lesion in mammography images based on region growing algorithm without using the threshold, the proposed method requires an initial rectangle surrounding the lesion selected manually by the radiologist (Region Of Interest), where the region growing algorithm applies on lines segments that attach each pixel of this rectangle with the seed point, such as the ends (seeds) of each line segment grow in a direction towards one another. The proposed approach is evaluated on a set of data with 20 masses of the MIAS base whose contours are annotated manually by expert radiologists. The performance of the method is evaluated in terms of specificity, sensitivity, accuracy and overlap. All the findings and details of approach are presented in detail.
Thyroid Cancer Detection for Myanmar Review and Recommendationijtsrd
Thyroid cancer is common cancer nowadays in most of the people irrespective of country, gender and regions, and so as in Myanmar. This paper brings significant work on thyroid cancer detection using image processing and segmentation methods so that appropriate work can be carried out for the benefit of people of Myanmar. The critical study of important papers gives challenges in existing work on cancer detection using several methods but there is not robust method for thyroid cancer detection. So, we attempt to suggest a robust set of methods for addressing this problem for Myanmar since there is very limited work on detection of cancer in the country. Hanni Htun | Moe Moe Htay | Sin Thi Yar Myint | Phyo Hay Mar Wai | Pyae Pyae Soe "Thyroid Cancer Detection for Myanmar: Review and Recommendation" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26723.pdfPaper URL: https://www.ijtsrd.com/computer-science/artificial-intelligence/26723/thyroid-cancer-detection-for-myanmar-review-and-recommendation/hanni-htun
Framework for progressive segmentation of chest radiograph for efficient diag...IJECEIAES
Segmentation is one of the most essential steps required to identify the inert object in the chest x-ray. A review with the existing segmentation techniques towards chest x-ray as well as other vital organs was performed. The main objective was to find whether existing system offers accuracy at the cost of recursive and complex operations. The proposed system contributes to introduce a framework that can offer a good balance between computational performance and segmentation performance. Given an input of chest x-ray, the system offers progressive search for similar image on the basis of similarity score with queried image. Region-based shape descriptor is applied for extracting the feature exclusively for identifying the lung region from the thoracic region followed by contour adjustment. The final segmentation outcome shows accurate identification followed by segmentation of apical and costophrenic region of lung. Comparative analysis proved that proposed system offers better segmentation performance in contrast to existing system.
ENHANCING SEGMENTATION APPROACHES FROM GAUSSIAN MIXTURE MODEL AND EXPECTED MA...Christo Ananth
Automatic liver tumor segmentation would bigly influence liver treatment organizing strategy and follow-up assessment, as a result of organization and joining of full picture information. Right now, develop a totally programmed technique for liver tumor division in CT picture. Introductory liver division comprises of applying a functioning form strategy. In the wake of separating liver applying Super pixel division Algorithm for portioning liver tumor proficiently. In the proposed work, we will investigate these procedures so as to improve division of various segments of the CT pictures. The exploratory outcomes indicated that the proposed strategy was exact for liver tumor division.
A New Approach to the Detection of Mammogram Boundary IJECEIAES
Mammography is a method used for the detection of breast cancer. computer-aided diagnostic (CAD) systems help the radiologist in the detection and interpretation of mass in breast mammography. One of the important information of a mass is its contour and its form because it provides valuable information about the abnormality of a mass. The accuracy in the recognition of the shape of a mass is related to the accuracy of the detected mass contours. In this work we propose a new approach for detecting the boundaries of lesion in mammography images based on region growing algorithm without using the threshold, the proposed method requires an initial rectangle surrounding the lesion selected manually by the radiologist (Region Of Interest), where the region growing algorithm applies on lines segments that attach each pixel of this rectangle with the seed point, such as the ends (seeds) of each line segment grow in a direction towards one another. The proposed approach is evaluated on a set of data with 20 masses of the MIAS base whose contours are annotated manually by expert radiologists. The performance of the method is evaluated in terms of specificity, sensitivity, accuracy and overlap. All the findings and details of approach are presented in detail.
Breast Cancer Detection from Mammography Images Using Machine Learning Algorithms (U-Net Segmentation and Dense Net Classifier implementation are in progress)
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.
A novel framework for efficient identification of brain cancer region from br...IJECEIAES
Diagnosis of brain cancer using existing imaging techniques, e.g., Magnetic Resonance Imaging (MRI) is shrouded with various degrees of challenges. At present, there are very few significant research models focusing on introducing some novel and unique solutions towards such problems of detection. Moreover, existing techniques are found to have lesser accuracy as compared to other detection schemes. Therefore, the proposed paper presents a framework that introduces a series of simple and computationally cost-effective techniques that have assisted in leveraging the accuracy level to a very higher degree. The proposed framework takes the input image and subjects it to non-conventional segmentation mechanism followed by optimizing the performance using directed acyclic graph, Bayesian Network, and neural network. The study outcome of the proposed system shows the significantly higher degree of accuracy in detection performance as compared to frequently existing approaches.
SVM &GA-CLUSTERING BASED FEATURE SELECTION APPROACH FOR BREAST CANCER DETECTIONijscai
Mortality leading among women in developed countries is breast cancer. Breast cancer is women's second most prominent cause of cancer mortality worldwide. In recent decades, women's high prevalence of breast cancer has risen dramatically. This paper discussed several data analysis methods used to detect breast cancer early. Breast cancer diagnosis distinguishes benign and malignant breast lumps. Using data processing tools, we tackled this disease analysis. Data mining is an important step of library discovery where intelligent methods are used to detect patterns. Several clinical breast cancer studies were conducted using soft computing and machine learning techniques. Sometimes their algorithms are easier, easier, or more comprehensive than others. This research is focused on genetic programming and machine learning algorithms to reliably identify benign and malignant breast cancer. This study aimed to optimise the testing algorithm. We used genetic programming methods to choose classification machines' best features and parameter values. Data mining is an important step of library discovery where intelligent methods are used to detect patterns. We are analysing data accessible from the U.C.I. deep-learning data set in Wisconsin. In this experiment, we equate four Weka clustering strategies with genetic clustering. A comparison of results reveals that sequential minimal optimization (S.M.O.) is better than I.B.K. and B.F. Tree processes, i.e. 97.71%.
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.
MEDICAL IMAGING MUTIFRACTAL ANALYSIS IN PREDICTION OF EFFICIENCY OF CANCER TH...csandit
Based on pressing need for predictive performance improvement, we explored the value of pretherapy
tumour histology image analysis to predict chemotherapy response. It was shown that
multifractal analysis of breast tumour tissue prior to chemotherapy indeed has the capacity to
distinguish between histological images of the different chemotherapy responder groups with
accuracies of 91.4% for pPR, 82.9% for pCR and 82.1% for PD/SD.
PERFORMANCE EVALUATION OF TUMOR DETECTION TECHNIQUES ijcsa
Automatic segmentation of tumor plays a vital role in diagnosis and surgical planning. This paper deals
with techniques which providing solution for detecting hepatic tumor in Computed Tomography (CT)
images. The main aim of this work is to analyze performance of tumor detection techniques like Knowledge
Based Constraints, Graph Cut Method and Gradient Vector Flow active contour. These three techniques
are computed using sensitivity, specificity and accuracy. From the evaluated result, knowledge based
constraints method is better than other graph cut method and gradient vector flow active contour.
USING DISTANCE MEASURE BASED CLASSIFICATION IN AUTOMATIC EXTRACTION OF LUNGS ...sipij
We introduce in this paper a reliable method for automatic extraction of lungs nodules from CT chest
images and shed the light on the details of using the Weighted Euclidean Distance (WED) for classifying
lungs connected components into nodule and not-nodule. We explain also using Connected Component
Labeling (CCL) in an effective and flexible method for extraction of lungs area from chest CT images with
a wide variety of shapes and sizes. This lungs extraction method makes use of, as well as CCL, some
morphological operations. Our tests have shown that the performance of the introduce method is high.
Finally, in order to check whether the method works correctly or not for healthy and patient CT images, we
tested the method by some images of healthy persons and demonstrated that the overall performance of the
method is satisfactory.
Radiomics: Novel Paradigm of Deep Learning for Clinical Decision Support towa...Wookjin Choi
‘Radiomics’ is a novel process to identify ‘radiome’ in the field of imaging informatics when long-term clinical outcomes such as mortality are not immediately available, relying on first acquiring paired gene expression data and medical images at diagnosis from a study cohort, and then leveraging the public gene expression data containing clinical outcomes from a closely matched population into a personalized medicine (Stanford and Harvard University).
SVM &GA-CLUSTERING BASED FEATURE SELECTION APPROACH FOR BREAST CANCER DETECTIONijscai
Mortality leading among women in developed countries is breast cancer. Breast cancer is women's second most prominent cause of cancer mortality worldwide. In recent decades, women's high prevalence of breast cancer has risen dramatically. This paper discussed several data analysis methods used to detect breast cancer early. Breast cancer diagnosis distinguishes benign and malignant breast lumps. Using data processing tools, we tackled this disease analysis. Data mining is an important step of library discovery where intelligent methods are used to detect patterns. Several clinical breast cancer studies were conducted using soft computing and machine learning techniques. Sometimes their algorithms are easier, easier, or more comprehensive than others. This research is focused on genetic programming and machine
learning algorithms to reliably identify benign and malignant breast cancer. This study aimed to optimise the testing algorithm. We used genetic programming methods to choose classification machines' best features and parameter values. Data mining is an important step of library discovery where intelligent methods are used to detect patterns. We are analysing data accessible from the U.C.I. deep-learning data
set in Wisconsin. In this experiment, we equate four Weka clustering strategies with genetic clustering. A comparison of results reveals that sequential minimal optimization (S.M.O.) is better than I.B.K. and B.F. Tree processes, i.e. 97.71%.
A review on data mining techniques for Digital Mammographic Analysisijdmtaiir
- Medical Data mining is the search for relationships
and patterns within the medical data that could provide useful
knowledge for effective medical diagnosis. The predictability
of disease will become more effective and early detection of
disease will aid in increased exposure to required patient care
and improved cure rates using computational applications.
Review shows that the reasons for feature selection include
improvement in performance prediction, reduction in
computational requirements, reduction in data storage
requirements, reduction in the cost of future measurements
and improvement in data or model understanding
BREAST CANCER DIAGNOSIS USING MACHINE LEARNING ALGORITHMS –A SURVEYijdpsjournal
Breast cancer has become a common factor now-a-days. Despite the fact, not all general hospitals
have the facilities to diagnose breast cancer through mammograms. Waiting for diagnosing a breast
cancer for a long time may increase the possibility of the cancer spreading. Therefore a computerized
breast cancer diagnosis has been developed to reduce the time taken to diagnose the breast cancer and
reduce the death rate. This paper summarizes the survey on breast cancer diagnosis using various machine
learning algorithms and methods, which are used to improve the accuracy of predicting cancer. This survey
can also help us to know about number of papers that are implemented to diagnose the breast cancer.
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.
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.
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.
Breast Cancer Detection from Mammography Images Using Machine Learning Algorithms (U-Net Segmentation and Dense Net Classifier implementation are in progress)
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.
A novel framework for efficient identification of brain cancer region from br...IJECEIAES
Diagnosis of brain cancer using existing imaging techniques, e.g., Magnetic Resonance Imaging (MRI) is shrouded with various degrees of challenges. At present, there are very few significant research models focusing on introducing some novel and unique solutions towards such problems of detection. Moreover, existing techniques are found to have lesser accuracy as compared to other detection schemes. Therefore, the proposed paper presents a framework that introduces a series of simple and computationally cost-effective techniques that have assisted in leveraging the accuracy level to a very higher degree. The proposed framework takes the input image and subjects it to non-conventional segmentation mechanism followed by optimizing the performance using directed acyclic graph, Bayesian Network, and neural network. The study outcome of the proposed system shows the significantly higher degree of accuracy in detection performance as compared to frequently existing approaches.
SVM &GA-CLUSTERING BASED FEATURE SELECTION APPROACH FOR BREAST CANCER DETECTIONijscai
Mortality leading among women in developed countries is breast cancer. Breast cancer is women's second most prominent cause of cancer mortality worldwide. In recent decades, women's high prevalence of breast cancer has risen dramatically. This paper discussed several data analysis methods used to detect breast cancer early. Breast cancer diagnosis distinguishes benign and malignant breast lumps. Using data processing tools, we tackled this disease analysis. Data mining is an important step of library discovery where intelligent methods are used to detect patterns. Several clinical breast cancer studies were conducted using soft computing and machine learning techniques. Sometimes their algorithms are easier, easier, or more comprehensive than others. This research is focused on genetic programming and machine learning algorithms to reliably identify benign and malignant breast cancer. This study aimed to optimise the testing algorithm. We used genetic programming methods to choose classification machines' best features and parameter values. Data mining is an important step of library discovery where intelligent methods are used to detect patterns. We are analysing data accessible from the U.C.I. deep-learning data set in Wisconsin. In this experiment, we equate four Weka clustering strategies with genetic clustering. A comparison of results reveals that sequential minimal optimization (S.M.O.) is better than I.B.K. and B.F. Tree processes, i.e. 97.71%.
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.
MEDICAL IMAGING MUTIFRACTAL ANALYSIS IN PREDICTION OF EFFICIENCY OF CANCER TH...csandit
Based on pressing need for predictive performance improvement, we explored the value of pretherapy
tumour histology image analysis to predict chemotherapy response. It was shown that
multifractal analysis of breast tumour tissue prior to chemotherapy indeed has the capacity to
distinguish between histological images of the different chemotherapy responder groups with
accuracies of 91.4% for pPR, 82.9% for pCR and 82.1% for PD/SD.
PERFORMANCE EVALUATION OF TUMOR DETECTION TECHNIQUES ijcsa
Automatic segmentation of tumor plays a vital role in diagnosis and surgical planning. This paper deals
with techniques which providing solution for detecting hepatic tumor in Computed Tomography (CT)
images. The main aim of this work is to analyze performance of tumor detection techniques like Knowledge
Based Constraints, Graph Cut Method and Gradient Vector Flow active contour. These three techniques
are computed using sensitivity, specificity and accuracy. From the evaluated result, knowledge based
constraints method is better than other graph cut method and gradient vector flow active contour.
USING DISTANCE MEASURE BASED CLASSIFICATION IN AUTOMATIC EXTRACTION OF LUNGS ...sipij
We introduce in this paper a reliable method for automatic extraction of lungs nodules from CT chest
images and shed the light on the details of using the Weighted Euclidean Distance (WED) for classifying
lungs connected components into nodule and not-nodule. We explain also using Connected Component
Labeling (CCL) in an effective and flexible method for extraction of lungs area from chest CT images with
a wide variety of shapes and sizes. This lungs extraction method makes use of, as well as CCL, some
morphological operations. Our tests have shown that the performance of the introduce method is high.
Finally, in order to check whether the method works correctly or not for healthy and patient CT images, we
tested the method by some images of healthy persons and demonstrated that the overall performance of the
method is satisfactory.
Radiomics: Novel Paradigm of Deep Learning for Clinical Decision Support towa...Wookjin Choi
‘Radiomics’ is a novel process to identify ‘radiome’ in the field of imaging informatics when long-term clinical outcomes such as mortality are not immediately available, relying on first acquiring paired gene expression data and medical images at diagnosis from a study cohort, and then leveraging the public gene expression data containing clinical outcomes from a closely matched population into a personalized medicine (Stanford and Harvard University).
SVM &GA-CLUSTERING BASED FEATURE SELECTION APPROACH FOR BREAST CANCER DETECTIONijscai
Mortality leading among women in developed countries is breast cancer. Breast cancer is women's second most prominent cause of cancer mortality worldwide. In recent decades, women's high prevalence of breast cancer has risen dramatically. This paper discussed several data analysis methods used to detect breast cancer early. Breast cancer diagnosis distinguishes benign and malignant breast lumps. Using data processing tools, we tackled this disease analysis. Data mining is an important step of library discovery where intelligent methods are used to detect patterns. Several clinical breast cancer studies were conducted using soft computing and machine learning techniques. Sometimes their algorithms are easier, easier, or more comprehensive than others. This research is focused on genetic programming and machine
learning algorithms to reliably identify benign and malignant breast cancer. This study aimed to optimise the testing algorithm. We used genetic programming methods to choose classification machines' best features and parameter values. Data mining is an important step of library discovery where intelligent methods are used to detect patterns. We are analysing data accessible from the U.C.I. deep-learning data
set in Wisconsin. In this experiment, we equate four Weka clustering strategies with genetic clustering. A comparison of results reveals that sequential minimal optimization (S.M.O.) is better than I.B.K. and B.F. Tree processes, i.e. 97.71%.
A review on data mining techniques for Digital Mammographic Analysisijdmtaiir
- Medical Data mining is the search for relationships
and patterns within the medical data that could provide useful
knowledge for effective medical diagnosis. The predictability
of disease will become more effective and early detection of
disease will aid in increased exposure to required patient care
and improved cure rates using computational applications.
Review shows that the reasons for feature selection include
improvement in performance prediction, reduction in
computational requirements, reduction in data storage
requirements, reduction in the cost of future measurements
and improvement in data or model understanding
BREAST CANCER DIAGNOSIS USING MACHINE LEARNING ALGORITHMS –A SURVEYijdpsjournal
Breast cancer has become a common factor now-a-days. Despite the fact, not all general hospitals
have the facilities to diagnose breast cancer through mammograms. Waiting for diagnosing a breast
cancer for a long time may increase the possibility of the cancer spreading. Therefore a computerized
breast cancer diagnosis has been developed to reduce the time taken to diagnose the breast cancer and
reduce the death rate. This paper summarizes the survey on breast cancer diagnosis using various machine
learning algorithms and methods, which are used to improve the accuracy of predicting cancer. This survey
can also help us to know about number of papers that are implemented to diagnose the breast cancer.
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.
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.
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.
The Evolution and Impact of Medical Science Journals in Advancing Healthcaresana473753
Medical science journals have evolved into essential tools for advancing healthcare by disseminating research findings, promoting evidence-based practices, and fostering collaboration. Their historical significance, role in evidence-based medicine, and adaptability to the digital age make them indispensable in the quest for improved healthcare outcomes. As they continue to evolve, medical science journals will play a vital role in shaping the future of medicine and healthcare worldwide.
"journals" refer to academic or professional publications that contain articles and research papers related to various aspects of the medical field. These journals serve as a platform for the dissemination of new medical knowledge, research findings, clinical studies, and expert opinions. They play a crucial role in advancing medical science, sharing best practices, and keeping healthcare professionals, researchers, and students informed about the latest developments in medicine and related disciplines.
Breast cancer is the leading cause of death for women worldwide. Cancer can be discovered early, lowering the rate of death. Machine learning techniques are a hot field of research, and they have been shown to be helpful in cancer prediction and early detection. The primary purpose of this research is to identify which machine learning algorithms are the most successful in predicting and diagnosing breast cancer, according to five criteria: specificity, sensitivity, precision, accuracy, and F1 score. The project is finished in the Anaconda environment, which uses Python's NumPy and SciPy numerical and scientific libraries as well as matplotlib and Pandas. In this study, the Wisconsin diagnostic breast cancer dataset was used to evaluate eleven machine learning classifiers: decision tree, quadratic discriminant analysis, AdaBoost, Bagging meta estimator, Extra randomized trees, Gaussian process classifier, Ridge, Gaussian nave Bayes, k-Nearest neighbors, multilayer perceptron, and support vector classifier. During performance analysis, extremely randomized trees outperformed all other classifiers with an F1-score of 96.77% after data collection and data analysis.
https://jst.org.in/index.html
Our journal has dynamic landscape of academia and industry, the pursuit of knowledge extends across multiple domains, creating a tapestry where engineering, management, science, and mathematics converge. Welcome to our international journal, where we embark on a journey through the realms of cutting-edge technologies and innovative marketing strategies.
An approach of cervical cancer diagnosis using class weighting and oversampli...TELKOMNIKA JOURNAL
Globally, cervical cancer caused 604,127 new cases and 341,831 deaths in 2020, according to the global cancer observatory. In addition, the number of cervical cancer patients who have no symptoms has grown recently. Therefore, giving patients early notice of the possibility of cervical cancer is a useful task since it would enable them to have a clear understanding of their health state. The use of artificial intelligence (AI), particularly in machine learning, in this work is continually uncovering cervical cancer. With the help of a logit model and a new deep learning technique, we hope to identify cervical cancer using patient-provided data. For better outcomes, we employ Keras deep learning and its technique, which includes class weighting and oversampling. In comparison to the actual diagnostic result, the experimental result with model accuracy is 94.18%, and it also demonstrates a successful logit model cervical cancer prediction.
Breast cancer classification with histopathological image based on machine le...IJECEIAES
Breast cancer represents one of the most common reasons for death in the worldwide. It has a substantially higher death rate than other types of cancer. Early detection can enhance the chances of receiving proper treatment and survival. In order to address this problem, this work has provided a convolutional neural network (CNN) deep learning (DL) based model on the classification that may be used to differentiate breast cancer histopathology images as benign or malignant. Besides that, five different types of pre-trained CNN architectures have been used to investigate the performance of the model to solve this problem which are the residual neural network-50 (ResNet-50), visual geometry group-19 (VGG-19), Inception-V3, and AlexNet while the ResNet-50 is also functions as a feature extractor to retrieve information from images and passed them to machine learning algorithms, in this case, a random forest (RF) and k-nearest neighbors (KNN) are employed for classification. In this paper, experiments are done using the BreakHis public dataset. As a result, the ResNet-50 network has the highest test accuracy of 97% to classify breast cancer images.
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.
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%.
Predictive modeling for breast cancer based on machine learning algorithms an...IJECEIAES
Breast cancer is one of the leading causes of death among women worldwide. However, early prediction of breast cancer plays a crucial role. Therefore, strong needs exist for automatic accurate early prediction of breast cancer. In this paper, machine learning (ML) classifiers combined with features selection methods are used to build an intelligent tool for breast cancer prediction. The Wisconsin diagnostic breast cancer (WDBC) dataset is used to train and test the model. Classification algorithms, including support vector machine (SVM), light gradient boosting machine (LightGBM), random forest (RF), logistic regression (LR), k-nearest neighbors (k-NN), and naïve Bayes, were employed. Performance measures for each of them were obtained, namely: accuracy, precision, recall, F-score, Kappa, Matthews correlation coefficient (MCC), and time. The results indicate that without feature selection, LightGBM achieves the highest accuracy at 95%. With minimum redundancy maximum relevance (mRMR) feature selection (15 features), LightGBM outperforms other classifiers, achieving an accuracy of 98%. For Pearson correlation coefficient feature selection (15 features), LightGBM also excels with a 95% accuracy rate. Lasso feature selection (5 features) produces varied results across classifiers, with logistic regression achieving the highest accuracy at 96%. These findings underscore the importance of feature selection in refining model performance and in improving detection for breast cancer.
A Review on Data Mining Techniques for Prediction of Breast Cancer RecurrenceDr. Amarjeet Singh
The most common type of cancer in women
worldwide is the Breast Cancer. Breast cancer may be
detected early using Mammograms, probably before it's
spread. Recurrent breast cancer could occur months or years
after initial treatment. The cancer could return within the
same place because the original cancer (local recurrence), or it
may spread to different areas of your body (distant
recurrence). Early stage treatment is done not only to cure
breast cancer however additionally facilitate in preventing its
repetition/recurrence. Data mining algorithms provide
assistance in predicting the early-stage breast cancer that
continually has been difficult analysis drawback. The
projected analysis can establish the most effective algorithm
that predicts the recurrence of the breast cancer and improve
the accuracy the algorithms. Large information like Clump,
Classification, Association Rules, Prediction and Neural
Networks, Decision Trees can be analyzed using data mining
applications and techniques.
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.
Similar to A novel approach to jointly address localization and classification of breast cancer using bio-inspired approach (20)
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
Enhancing battery system identification: nonlinear autoregressive modeling fo...IJECEIAES
Precisely characterizing Li-ion batteries is essential for optimizing their
performance, enhancing safety, and prolonging their lifespan across various
applications, such as electric vehicles and renewable energy systems. This
article introduces an innovative nonlinear methodology for system
identification of a Li-ion battery, employing a nonlinear autoregressive with
exogenous inputs (NARX) model. The proposed approach integrates the
benefits of nonlinear modeling with the adaptability of the NARX structure,
facilitating a more comprehensive representation of the intricate
electrochemical processes within the battery. Experimental data collected
from a Li-ion battery operating under diverse scenarios are employed to
validate the effectiveness of the proposed methodology. The identified
NARX model exhibits superior accuracy in predicting the battery's behavior
compared to traditional linear models. This study underscores the
importance of accounting for nonlinearities in battery modeling, providing
insights into the intricate relationships between state-of-charge, voltage, and
current under dynamic conditions.
Smart grid deployment: from a bibliometric analysis to a surveyIJECEIAES
Smart grids are one of the last decades' innovations in electrical energy.
They bring relevant advantages compared to the traditional grid and
significant interest from the research community. Assessing the field's
evolution is essential to propose guidelines for facing new and future smart
grid challenges. In addition, knowing the main technologies involved in the
deployment of smart grids (SGs) is important to highlight possible
shortcomings that can be mitigated by developing new tools. This paper
contributes to the research trends mentioned above by focusing on two
objectives. First, a bibliometric analysis is presented to give an overview of
the current research level about smart grid deployment. Second, a survey of
the main technological approaches used for smart grid implementation and
their contributions are highlighted. To that effect, we searched the Web of
Science (WoS), and the Scopus databases. We obtained 5,663 documents
from WoS and 7,215 from Scopus on smart grid implementation or
deployment. With the extraction limitation in the Scopus database, 5,872 of
the 7,215 documents were extracted using a multi-step process. These two
datasets have been analyzed using a bibliometric tool called bibliometrix.
The main outputs are presented with some recommendations for future
research.
Use of analytical hierarchy process for selecting and prioritizing islanding ...IJECEIAES
One of the problems that are associated to power systems is islanding
condition, which must be rapidly and properly detected to prevent any
negative consequences on the system's protection, stability, and security.
This paper offers a thorough overview of several islanding detection
strategies, which are divided into two categories: classic approaches,
including local and remote approaches, and modern techniques, including
techniques based on signal processing and computational intelligence.
Additionally, each approach is compared and assessed based on several
factors, including implementation costs, non-detected zones, declining
power quality, and response times using the analytical hierarchy process
(AHP). The multi-criteria decision-making analysis shows that the overall
weight of passive methods (24.7%), active methods (7.8%), hybrid methods
(5.6%), remote methods (14.5%), signal processing-based methods (26.6%),
and computational intelligent-based methods (20.8%) based on the
comparison of all criteria together. Thus, it can be seen from the total weight
that hybrid approaches are the least suitable to be chosen, while signal
processing-based methods are the most appropriate islanding detection
method to be selected and implemented in power system with respect to the
aforementioned factors. Using Expert Choice software, the proposed
hierarchy model is studied and examined.
Enhancing of single-stage grid-connected photovoltaic system using fuzzy logi...IJECEIAES
The power generated by photovoltaic (PV) systems is influenced by
environmental factors. This variability hampers the control and utilization of
solar cells' peak output. In this study, a single-stage grid-connected PV
system is designed to enhance power quality. Our approach employs fuzzy
logic in the direct power control (DPC) of a three-phase voltage source
inverter (VSI), enabling seamless integration of the PV connected to the
grid. Additionally, a fuzzy logic-based maximum power point tracking
(MPPT) controller is adopted, which outperforms traditional methods like
incremental conductance (INC) in enhancing solar cell efficiency and
minimizing the response time. Moreover, the inverter's real-time active and
reactive power is directly managed to achieve a unity power factor (UPF).
The system's performance is assessed through MATLAB/Simulink
implementation, showing marked improvement over conventional methods,
particularly in steady-state and varying weather conditions. For solar
irradiances of 500 and 1,000 W/m2
, the results show that the proposed
method reduces the total harmonic distortion (THD) of the injected current
to the grid by approximately 46% and 38% compared to conventional
methods, respectively. Furthermore, we compare the simulation results with
IEEE standards to evaluate the system's grid compatibility.
Enhancing photovoltaic system maximum power point tracking with fuzzy logic-b...IJECEIAES
Photovoltaic systems have emerged as a promising energy resource that
caters to the future needs of society, owing to their renewable, inexhaustible,
and cost-free nature. The power output of these systems relies on solar cell
radiation and temperature. In order to mitigate the dependence on
atmospheric conditions and enhance power tracking, a conventional
approach has been improved by integrating various methods. To optimize
the generation of electricity from solar systems, the maximum power point
tracking (MPPT) technique is employed. To overcome limitations such as
steady-state voltage oscillations and improve transient response, two
traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb
and observe (P&O), have been modified. This research paper aims to
simulate and validate the step size of the proposed modified P&O and FLC
techniques within the MPPT algorithm using MATLAB/Simulink for
efficient power tracking in photovoltaic systems.
Adaptive synchronous sliding control for a robot manipulator based on neural ...IJECEIAES
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
robot hands is always an attractive topic in the research community. This is a
challenging problem because robot manipulators are complex nonlinear systems
and are often subject to fluctuations in loads and external disturbances. This
article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller
ensures that the positions of the joints track the desired trajectory, synchronize
the errors, and significantly reduces chattering. First, the synchronous tracking
errors and synchronous sliding surfaces are presented. Second, the synchronous
tracking error dynamics are determined. Third, a robust adaptive control law is
designed,the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy
logic. The built algorithm ensures that the tracking and approximation errors
are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results.
Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is
significantly reduced.
Remote field-programmable gate array laboratory for signal acquisition and de...IJECEIAES
A remote laboratory utilizing field-programmable gate array (FPGA) technologies enhances students’ learning experience anywhere and anytime in embedded system design. Existing remote laboratories prioritize hardware access and visual feedback for observing board behavior after programming, neglecting comprehensive debugging tools to resolve errors that require internal signal acquisition. This paper proposes a novel remote embeddedsystem design approach targeting FPGA technologies that are fully interactive via a web-based platform. Our solution provides FPGA board access and debugging capabilities beyond the visual feedback provided by existing remote laboratories. We implemented a lab module that allows users to seamlessly incorporate into their FPGA design. The module minimizes hardware resource utilization while enabling the acquisition of a large number of data samples from the signal during the experiments by adaptively compressing the signal prior to data transmission. The results demonstrate an average compression ratio of 2.90 across three benchmark signals, indicating efficient signal acquisition and effective debugging and analysis. This method allows users to acquire more data samples than conventional methods. The proposed lab allows students to remotely test and debug their designs, bridging the gap between theory and practice in embedded system design.
Detecting and resolving feature envy through automated machine learning and m...IJECEIAES
Efficiently identifying and resolving code smells enhances software project quality. This paper presents a novel solution, utilizing automated machine learning (AutoML) techniques, to detect code smells and apply move method refactoring. By evaluating code metrics before and after refactoring, we assessed its impact on coupling, complexity, and cohesion. Key contributions of this research include a unique dataset for code smell classification and the development of models using AutoGluon for optimal performance. Furthermore, the study identifies the top 20 influential features in classifying feature envy, a well-known code smell, stemming from excessive reliance on external classes. We also explored how move method refactoring addresses feature envy, revealing reduced coupling and complexity, and improved cohesion, ultimately enhancing code quality. In summary, this research offers an empirical, data-driven approach, integrating AutoML and move method refactoring to optimize software project quality. Insights gained shed light on the benefits of refactoring on code quality and the significance of specific features in detecting feature envy. Future research can expand to explore additional refactoring techniques and a broader range of code metrics, advancing software engineering practices and standards.
Smart monitoring technique for solar cell systems using internet of things ba...IJECEIAES
Rapidly and remotely monitoring and receiving the solar cell systems status parameters, solar irradiance, temperature, and humidity, are critical issues in enhancement their efficiency. Hence, in the present article an improved smart prototype of internet of things (IoT) technique based on embedded system through NodeMCU ESP8266 (ESP-12E) was carried out experimentally. Three different regions at Egypt; Luxor, Cairo, and El-Beheira cities were chosen to study their solar irradiance profile, temperature, and humidity by the proposed IoT system. The monitoring data of solar irradiance, temperature, and humidity were live visualized directly by Ubidots through hypertext transfer protocol (HTTP) protocol. The measured solar power radiation in Luxor, Cairo, and El-Beheira ranged between 216-1000, 245-958, and 187-692 W/m 2 respectively during the solar day. The accuracy and rapidity of obtaining monitoring results using the proposed IoT system made it a strong candidate for application in monitoring solar cell systems. On the other hand, the obtained solar power radiation results of the three considered regions strongly candidate Luxor and Cairo as suitable places to build up a solar cells system station rather than El-Beheira.
An efficient security framework for intrusion detection and prevention in int...IJECEIAES
Over the past few years, the internet of things (IoT) has advanced to connect billions of smart devices to improve quality of life. However, anomalies or malicious intrusions pose several security loopholes, leading to performance degradation and threat to data security in IoT operations. Thereby, IoT security systems must keep an eye on and restrict unwanted events from occurring in the IoT network. Recently, various technical solutions based on machine learning (ML) models have been derived towards identifying and restricting unwanted events in IoT. However, most ML-based approaches are prone to miss-classification due to inappropriate feature selection. Additionally, most ML approaches applied to intrusion detection and prevention consider supervised learning, which requires a large amount of labeled data to be trained. Consequently, such complex datasets are impossible to source in a large network like IoT. To address this problem, this proposed study introduces an efficient learning mechanism to strengthen the IoT security aspects. The proposed algorithm incorporates supervised and unsupervised approaches to improve the learning models for intrusion detection and mitigation. Compared with the related works, the experimental outcome shows that the model performs well in a benchmark dataset. It accomplishes an improved detection accuracy of approximately 99.21%.
Developing a smart system for infant incubators using the internet of things ...IJECEIAES
This research is developing an incubator system that integrates the internet of things and artificial intelligence to improve care for premature babies. The system workflow starts with sensors that collect data from the incubator. Then, the data is sent in real-time to the internet of things (IoT) broker eclipse mosquito using the message queue telemetry transport (MQTT) protocol version 5.0. After that, the data is stored in a database for analysis using the long short-term memory network (LSTM) method and displayed in a web application using an application programming interface (API) service. Furthermore, the experimental results produce as many as 2,880 rows of data stored in the database. The correlation coefficient between the target attribute and other attributes ranges from 0.23 to 0.48. Next, several experiments were conducted to evaluate the model-predicted value on the test data. The best results are obtained using a two-layer LSTM configuration model, each with 60 neurons and a lookback setting 6. This model produces an R 2 value of 0.934, with a root mean square error (RMSE) value of 0.015 and a mean absolute error (MAE) of 0.008. In addition, the R 2 value was also evaluated for each attribute used as input, with a result of values between 0.590 and 0.845.
A review on internet of things-based stingless bee's honey production with im...IJECEIAES
Honey is produced exclusively by honeybees and stingless bees which both are well adapted to tropical and subtropical regions such as Malaysia. Stingless bees are known for producing small amounts of honey and are known for having a unique flavor profile. Problem identified that many stingless bees collapsed due to weather, temperature and environment. It is critical to understand the relationship between the production of stingless bee honey and environmental conditions to improve honey production. Thus, this paper presents a review on stingless bee's honey production and prediction modeling. About 54 previous research has been analyzed and compared in identifying the research gaps. A framework on modeling the prediction of stingless bee honey is derived. The result presents the comparison and analysis on the internet of things (IoT) monitoring systems, honey production estimation, convolution neural networks (CNNs), and automatic identification methods on bee species. It is identified based on image detection method the top best three efficiency presents CNN is at 98.67%, densely connected convolutional networks with YOLO v3 is 97.7%, and DenseNet201 convolutional networks 99.81%. This study is significant to assist the researcher in developing a model for predicting stingless honey produced by bee's output, which is important for a stable economy and food security.
A trust based secure access control using authentication mechanism for intero...IJECEIAES
The internet of things (IoT) is a revolutionary innovation in many aspects of our society including interactions, financial activity, and global security such as the military and battlefield internet. Due to the limited energy and processing capacity of network devices, security, energy consumption, compatibility, and device heterogeneity are the long-term IoT problems. As a result, energy and security are critical for data transmission across edge and IoT networks. Existing IoT interoperability techniques need more computation time, have unreliable authentication mechanisms that break easily, lose data easily, and have low confidentiality. In this paper, a key agreement protocol-based authentication mechanism for IoT devices is offered as a solution to this issue. This system makes use of information exchange, which must be secured to prevent access by unauthorized users. Using a compact contiki/cooja simulator, the performance and design of the suggested framework are validated. The simulation findings are evaluated based on detection of malicious nodes after 60 minutes of simulation. The suggested trust method, which is based on privacy access control, reduced packet loss ratio to 0.32%, consumed 0.39% power, and had the greatest average residual energy of 0.99 mJoules at 10 nodes.
Fuzzy linear programming with the intuitionistic polygonal fuzzy numbersIJECEIAES
In real world applications, data are subject to ambiguity due to several factors; fuzzy sets and fuzzy numbers propose a great tool to model such ambiguity. In case of hesitation, the complement of a membership value in fuzzy numbers can be different from the non-membership value, in which case we can model using intuitionistic fuzzy numbers as they provide flexibility by defining both a membership and a non-membership functions. In this article, we consider the intuitionistic fuzzy linear programming problem with intuitionistic polygonal fuzzy numbers, which is a generalization of the previous polygonal fuzzy numbers found in the literature. We present a modification of the simplex method that can be used to solve any general intuitionistic fuzzy linear programming problem after approximating the problem by an intuitionistic polygonal fuzzy number with n edges. This method is given in a simple tableau formulation, and then applied on numerical examples for clarity.
The performance of artificial intelligence in prostate magnetic resonance im...IJECEIAES
Prostate cancer is the predominant form of cancer observed in men worldwide. The application of magnetic resonance imaging (MRI) as a guidance tool for conducting biopsies has been established as a reliable and well-established approach in the diagnosis of prostate cancer. The diagnostic performance of MRI-guided prostate cancer diagnosis exhibits significant heterogeneity due to the intricate and multi-step nature of the diagnostic pathway. The development of artificial intelligence (AI) models, specifically through the utilization of machine learning techniques such as deep learning, is assuming an increasingly significant role in the field of radiology. In the realm of prostate MRI, a considerable body of literature has been dedicated to the development of various AI algorithms. These algorithms have been specifically designed for tasks such as prostate segmentation, lesion identification, and classification. The overarching objective of these endeavors is to enhance diagnostic performance and foster greater agreement among different observers within MRI scans for the prostate. This review article aims to provide a concise overview of the application of AI in the field of radiology, with a specific focus on its utilization in prostate MRI.
Seizure stage detection of epileptic seizure using convolutional neural networksIJECEIAES
According to the World Health Organization (WHO), seventy million individuals worldwide suffer from epilepsy, a neurological disorder. While electroencephalography (EEG) is crucial for diagnosing epilepsy and monitoring the brain activity of epilepsy patients, it requires a specialist to examine all EEG recordings to find epileptic behavior. This procedure needs an experienced doctor, and a precise epilepsy diagnosis is crucial for appropriate treatment. To identify epileptic seizures, this study employed a convolutional neural network (CNN) based on raw scalp EEG signals to discriminate between preictal, ictal, postictal, and interictal segments. The possibility of these characteristics is explored by examining how well timedomain signals work in the detection of epileptic signals using intracranial Freiburg Hospital (FH), scalp Children's Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) databases, and Temple University Hospital (TUH) EEG. To test the viability of this approach, two types of experiments were carried out. Firstly, binary class classification (preictal, ictal, postictal each versus interictal) and four-class classification (interictal versus preictal versus ictal versus postictal). The average accuracy for stage detection using CHB-MIT database was 84.4%, while the Freiburg database's time-domain signals had an accuracy of 79.7% and the highest accuracy of 94.02% for classification in the TUH EEG database when comparing interictal stage to preictal stage.
Analysis of driving style using self-organizing maps to analyze driver behaviorIJECEIAES
Modern life is strongly associated with the use of cars, but the increase in acceleration speeds and their maneuverability leads to a dangerous driving style for some drivers. In these conditions, the development of a method that allows you to track the behavior of the driver is relevant. The article provides an overview of existing methods and models for assessing the functioning of motor vehicles and driver behavior. Based on this, a combined algorithm for recognizing driving style is proposed. To do this, a set of input data was formed, including 20 descriptive features: About the environment, the driver's behavior and the characteristics of the functioning of the car, collected using OBD II. The generated data set is sent to the Kohonen network, where clustering is performed according to driving style and degree of danger. Getting the driving characteristics into a particular cluster allows you to switch to the private indicators of an individual driver and considering individual driving characteristics. The application of the method allows you to identify potentially dangerous driving styles that can prevent accidents.
Hyperspectral object classification using hybrid spectral-spatial fusion and ...IJECEIAES
Because of its spectral-spatial and temporal resolution of greater areas, hyperspectral imaging (HSI) has found widespread application in the field of object classification. The HSI is typically used to accurately determine an object's physical characteristics as well as to locate related objects with appropriate spectral fingerprints. As a result, the HSI has been extensively applied to object identification in several fields, including surveillance, agricultural monitoring, environmental research, and precision agriculture. However, because of their enormous size, objects require a lot of time to classify; for this reason, both spectral and spatial feature fusion have been completed. The existing classification strategy leads to increased misclassification, and the feature fusion method is unable to preserve semantic object inherent features; This study addresses the research difficulties by introducing a hybrid spectral-spatial fusion (HSSF) technique to minimize feature size while maintaining object intrinsic qualities; Lastly, a soft-margins kernel is proposed for multi-layer deep support vector machine (MLDSVM) to reduce misclassification. The standard Indian pines dataset is used for the experiment, and the outcome demonstrates that the HSSF-MLDSVM model performs substantially better in terms of accuracy and Kappa coefficient.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
Courier management system project report.pdfKamal Acharya
It is now-a-days very important for the people to send or receive articles like imported furniture, electronic items, gifts, business goods and the like. People depend vastly on different transport systems which mostly use the manual way of receiving and delivering the articles. There is no way to track the articles till they are received and there is no way to let the customer know what happened in transit, once he booked some articles. In such a situation, we need a system which completely computerizes the cargo activities including time to time tracking of the articles sent. This need is fulfilled by Courier Management System software which is online software for the cargo management people that enables them to receive the goods from a source and send them to a required destination and track their status from time to time.
Quality defects in TMT Bars, Possible causes and Potential Solutions.PrashantGoswami42
Maintaining high-quality standards in the production of TMT bars is crucial for ensuring structural integrity in construction. Addressing common defects through careful monitoring, standardized processes, and advanced technology can significantly improve the quality of TMT bars. Continuous training and adherence to quality control measures will also play a pivotal role in minimizing these defects.
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
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
COLLEGE BUS MANAGEMENT SYSTEM PROJECT REPORT.pdfKamal Acharya
The College Bus Management system is completely developed by Visual Basic .NET Version. The application is connect with most secured database language MS SQL Server. The application is develop by using best combination of front-end and back-end languages. The application is totally design like flat user interface. This flat user interface is more attractive user interface in 2017. The application is gives more important to the system functionality. The application is to manage the student’s details, driver’s details, bus details, bus route details, bus fees details and more. The application has only one unit for admin. The admin can manage the entire application. The admin can login into the application by using username and password of the admin. The application is develop for big and small colleges. It is more user friendly for non-computer person. Even they can easily learn how to manage the application within hours. The application is more secure by the admin. The system will give an effective output for the VB.Net and SQL Server given as input to the system. The compiled java program given as input to the system, after scanning the program will generate different reports. The application generates the report for users. The admin can view and download the report of the data. The application deliver the excel format reports. Because, excel formatted reports is very easy to understand the income and expense of the college bus. This application is mainly develop for windows operating system users. In 2017, 73% of people enterprises are using windows operating system. So the application will easily install for all the windows operating system users. The application-developed size is very low. The application consumes very low space in disk. Therefore, the user can allocate very minimum local disk space for this application.
Forklift Classes Overview by Intella PartsIntella Parts
Discover the different forklift classes and their specific applications. Learn how to choose the right forklift for your needs to ensure safety, efficiency, and compliance in your operations.
For more technical information, visit our website https://intellaparts.com
Automobile Management System Project Report.pdfKamal Acharya
The proposed project is developed to manage the automobile in the automobile dealer company. The main module in this project is login, automobile management, customer management, sales, complaints and reports. The first module is the login. The automobile showroom owner should login to the project for usage. The username and password are verified and if it is correct, next form opens. If the username and password are not correct, it shows the error message.
When a customer search for a automobile, if the automobile is available, they will be taken to a page that shows the details of the automobile including automobile name, automobile ID, quantity, price etc. “Automobile Management System” is useful for maintaining automobiles, customers effectively and hence helps for establishing good relation between customer and automobile organization. It contains various customized modules for effectively maintaining automobiles and stock information accurately and safely.
When the automobile is sold to the customer, stock will be reduced automatically. When a new purchase is made, stock will be increased automatically. While selecting automobiles for sale, the proposed software will automatically check for total number of available stock of that particular item, if the total stock of that particular item is less than 5, software will notify the user to purchase the particular item.
Also when the user tries to sale items which are not in stock, the system will prompt the user that the stock is not enough. Customers of this system can search for a automobile; can purchase a automobile easily by selecting fast. On the other hand the stock of automobiles can be maintained perfectly by the automobile shop manager overcoming the drawbacks of existing system.
2. Int J Elec & Comp Eng ISSN: 2088-8708
A novel approach to jointly address localization and classification of breast cancer using… (Sushma S. J.)
993
existing approaches, etc. It was also found that segmentation is always a common technique involved in both
the process of localization and classification of cancer. Apart from these entire problems, the situation of
classification turns most adverse if the classification / detection are supposed to be carried out in early stage
of cancer where there is no significant definition of cancerous region for the given medical image. At present,
majority of the analysis and investigation is carried out in by manually selection the region bearing the
clinical significance e.g. region of interest. Although adoption of region of interest offers good narrow down
of the investigation toward finding the cancerous site but there is no denying the fact that it is highly manual
process and is justified for only those images that requires special attention from the physician or radiologist.
It is due to practical implementation of region-of interest for diagnosis hundreds of medical image quite not
possible in real-world scenario and this problem can be only solved if the system is capable of identifying the
region of the image characterized by cancer. Hence, the practical application will always demand an
automatic detection and classification process to perform diagnosis of breast cancer efficiently. The practical
parameters to justify such performance in real time are always the response time and accuracy. The proposed
manuscript introduces a novel optimization technique that harnesses the potential of bio-inspired algorithm.
The contribution of the proposed study is that it offers solution by jointly addressing the problems of
detection and classification of breast cancer. The study also implements a rule-set based approach in order to
make a user-friendly classification of the breast cancer. Section 1.1 discusses about the existing literatures
where different techniques are discussed for detection as well as classification schemes used in diagnosisof
early stage of breast cancer followed by discussion of research problems associated with the existing system
in Section 1.2 and proposed solution in 1.3. Section 2 discusses about algorithm implementation associated
with the localization and classification process followed by discussion of result analysis with respect to visual
and comparative analysis in Section 3 using standard performance parameters to assess the proposition.
Finally, the conclusive remarks are provided in Section 4.
1.1. Background
This section is a continuation of our prior review work towards approaches of breast cancer
detection [10]. Beevi et al. [11] have presented a classifier design using deep belief network for assisting in
segementation and classification of a typical stage of mitosis in cancer progress stage with approximately
85% of accuracy performance. Similar adoption of advanced machine learning was witnessed in the work of
Carneiro et al. [12] who have used deep learning approach in order to perform classification along with
segmentation of lesions on breast image. Classification problem with respect to mass is also addressed in the
work of Chokri and Farida [13] where multi-layer perceptron is utilized. Duraisamy and Emperumal [14]
have used deep learning approach in order to perform classification for a given mammogram. The authors
have also used convolution neural network in order to carry out learning process. Elmoufidi et al. [15] have
implemented a multiple-instance learning method for facilitating segmentation from pixel-level as well as
classification from image-level using region-of-interest. Study towards classifier design was implemented by
Manivannan et al. [16] as well as Mercan et al. [17] using learning-based method over multiple instances in
order to perform classification. Nizam et al. [18] have carried out spectral methods in order to perform
estimation of the spacing from the images obtained from the ultrasound. Rabidas et al. [19] have carried out
analysis of classification problem with the help of Ripplet-II transformation technique by quantifying the
textural features. Reis et al. [20] have used region-of-interest scheme as well as feature extraction using
multiscale-based approach. Saha and Chakraborty [21] addressed the classification problem using deep
learning approach along with a segmentation being carried out using semantics. Usage of fisher vector
towards facilitating classification of image is carried out by Song et al. [22]. However, the process of
classification potential depends upon how strong is the detetion process. There are certain studies carried out
towards detections for ensuring better classification process. Strackx et al. [23] have introduced a hardware-
based approach for implementating a unique subsampling process for facilitating identification of breast
cancer. Investigation of cancer using breast phantoms using microwave imagery was carried out by Wang et
al. [24], [25] where the authors have considered time-domain analysis. Yin et al. [26] have implemented a
correlation-based method for enhancing the image analysis for breast cancer detection. Hossain and Mohan
[27] have used an analytical technique along with consideration of time-domain to find efficient detection of
cancer over microwave imaging. Jalilvand et al. [28] have used specific design of bowtie antenna in order to
perform detection of the breast cancer. The work carried out by Kwon et al [29] have used Gaussian band-
pass filtering in order to perform detection of cancer using three dimensional image. Adoption of S-
transforms is reported to upgrade the classification process as discussed by Beura et al. [30]. The authors
have also implemented AdaBoost algorithm along with random forest to enhance the classification process.
The next section discusses about the research problems associated with existing techniques. Sakthi et al. [31]
The Unit Commitment (UC) issue has been prepared by incorporates wind energy generators along with
thermal power method.
3. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 9, No. 2, April 2019 : 992 - 1001
994
Hamaine et al. [32] demonstrates the proposed method precisely differentiate standard brain images
from the irregular ones and benign lesions from malignant tumors. Similarly Bangare et al. [33] illustrated
and used to look for the targeted significance along with revealing the best -focused graphic location by way
of aliasing search technique included with novel “Neuroendoscopy Adapter Module (NAM)” method.
1.2. The problem
The significant research problems are as follows:
1) Existing approaches doesn‟t offer equal emphasis on jointly addressing the problems of detection and
classification of breast cancer.
2) There are less benchmarked model to prove efficiency of classification approach with respect to
simplistic and cost effective computational modeling.
3) Adoption of all existing machine learning offer increase precision but at the cost of resource and
training dependencies thereby minimizing the practical utility.
4) Majority of the existing mechanism has manual selection of observation area and there are less
involuntary techniques to support this phenomenon.
Therefore, the problem statement of the proposed study can be stated as “Developing a cost effective
computational modeling for jointly addressing the localization and classification problems associated with
breast cancer diagnosis is still an open challenge”. The next section outlines solution to this issue.
1.3. Proposed solution
The proposed work is a continuation of our prior implementation [34] and [35]. In the present work,
an integrated framework is modeled that is meant for addressing the joint problems associated with
localization as well as classification in breast cancer. The implementation of the proposed system is carried
out considering analytical research methodology. The schematic flow of the proposed system is as follows:
Localization
Problem
Classification
Problem
Auto-
segmentation
Multi-layer
enhancement Perform
Localization
Thresholding
Objective Function
(Bio-Inspired)
Elimination
Surplus Region
Normalization
Pectoral Muscle
Apply DWT
Bio-inspired
optimization
Apply Rule-Set
Perform Binary Classification
Figure 1. Schematic flow of proposed system
Figure 1 highlights that proposed system first address the localization problem and then the
classification problem. In order to address localization process, a multi-layer enhancement is beign carried
out with an aid of thresholding and using bio-inspired based implication of objective function.
The classification problem is sorted by performing elimination of the surplus region followed by
normalization and removal of the pectoral muscle. The outcome is further subjected to discrete wavelet
transformation in order to extract decomposed wavelets as the feature. The process is than subjected to the
bio-inspired based optimization principle that results in better selection of regions with most probability of
features bearing cancerous region. The inferencing of the outcome is carried out by applying rule-set that
significant assists in performing binary classification process. Therefore, the proposed system offers a
progressive mechanism to address both the problems in order to offer better classification performance.
The outcomes are made with respect to binarized classification in the form of malignant (abnormal) and
benign (normal) state of breast cancer. The next section outlines algorithm implementation.
4. Int J Elec & Comp Eng ISSN: 2088-8708
A novel approach to jointly address localization and classification of breast cancer using… (Sushma S. J.)
995
2. ALGORITHM IMPLEMENTATION
The primary function of the core algorithm is to ensure an effective detection followed by
classification of the breast cancer from the captured medical image. The proposed algorithm uses bio-
inspired algorithmic approach for designing the proposed algorithm. The complete operation of the algorithm
is discussed with respect to algorithm design for localizing the area of breast cancer and algorithm for
binarized classification of the breast cancer as following:
2.1. Algorithm design for localizing the area of breast cancer
This algorithm is responsible for localizing the exact position of the cancer in the breast cancer for a
given medical image. Applying the method of simple bio-inspired technique, the algorithm takes the input of
I (input image) and yield the outcome of Iloc (Output image) with identification of the cancerous region.
The steps of the algorithm are as following:
Algorithm for Localizing the Area of Breast Cancer
Input: I (Input Image)
Output: Iloc (Output Image)
Start
1. init I
2. Isegf1(Th(I))
3. Iprimf2(Iseg)
4. Isecf3(Iprim)
5. Iterf4(Isec)
6. Iopf5(Iter)
End
The description of the algorithm is as follows: After taking the input of medical image (Line-1),
it is subjected to different explicit functions to carry out different processing. The algorithm introduces a
function f1(x) that is essentially meant for carrying out involuntary segmentaion process (Line-2).
This function implements a cut-off operator Th over the input image I followed by obtaining binarized image
in order to construct a suitable mask. The algorithm than onstricts the highest possible mask and continue
labeling it followed by the concatenation of all the area. This operation leads to the maximum value of the
concatenated area of the mask. The segmentation is carried out over original image as well on masked image
ensuring that only one specification of mask is considered. The proposed algorithm also performs a
simplified operation using another function f2(x) that takes in the input of segmented image. The algorithm
first performs local contrast modification considering input arguments of processed image and weight factor.
(By processed image, it will mean applying unsigned integer of 8 bits on input image followed by altering the
precision to double). The obtained image from the local contrast modification is then subjected to the entropy
formulation followed by applying sobel operator to obtain prominent edges. An objective function is
defined as:
MN
EE
.)).log(log(
(1)
In the above expression (1), an empirical exression of objective function α is constructed that
considers the edge components E obtained from summation of all the edges using sobel operator of the area
obtained after local contrast modification. Applying a simple bio-inspired approach, if α>gbest, than the
original value of α is considered as the gbest or else a probability value [0.1-1] is assigned to the pbest.
The next step of the algorithm is to apply the secondary enhancement using function f3(x) that is developed
on the basis of threshold optimization (Line-4). The algorithm computes the probability as well as histogram
for the actual input image followed by initialization of mean and weight factor. Computation of variance is
carried out and only the variance matching with threshold is considered for the further computation.
This summation of maximum value of this new variance is used for obtaining the new threshold value.
The outcome is then subjected to the tertiary enhancement using a function f4(x). In this case, the outcome
image Isec is subjected to binarization followed by checking the situation when the value of the binarized
image is more than 10, which is only the case of either lump or nodule in the breast tissue. The final function
f5(x) is applied to ensure that the region infected with cancer is identified (Line-4 and Line-5). A slight
amount of recursive function is designed to apply probability to ascertain that there is a regular update of the
threshold parameter in order to ensure a better for of identification process of the region detected with the
5. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 9, No. 2, April 2019 : 992 - 1001
996
cancer. Another interesting fact of the proposed algorithm is that it offers a significant insights of the higher
contrastive region to be normal tissue or cancer-inflicted tissue in order to ensure that a simple and accuracy
identification of carried out using bio-inspired approach.
2.2. Algorithm for binarized classification of the breast cancer
The prior algorithm contributes in carrying out localization of the region infected with breast cancer
while this algorithm assist to carry out a simple classification technique by evolving up with a simple and
novel bio-inspired approach. This algorithm is essential for implementing a novel bio-inspred approach for
carrying out identification followed by binarized classification of the outcome. The algorithm takes in the
input of input image that after processing results in CC (Center of cluster) and Iout (classification outcome).
The steps of the algorithm are as follows:
Algorithm for Binarized Classification of the Breast Cancer
Input: I (input image)
Output: CC (Center of cluster), Iout (classification outcome)
Start
1. If6(I)
2. If8(prec(f7(I)))
3. If j>c.maxI
4. (s1, s2)=(s1, s2)+(j.h(idx), h(idx))
5. End
6. For k=1:n
7. [p1, p2][Rw(idx1), Rw(~idx1)]
8. For l=xiT
9. idx=idx+1
10. If l>T
11. (s1p, s2p)=(s1p, s2p)+(l.h(idx), h(idx))
12. End
13. If l<T && l>(T-c1.maxI)
14. s1b, s2b)=(s1b, s2b)+(j.h(idx), h(idx))
15. End
16. End
17. Iop (xwin-10:xwin+10, 1:Ay)=Iseg
18. If Fit<Pbest
19. [SIpart Pbest]CC & Pbest=Fit
20. Else
21. store prior value of SIpart
22. If Pbest<Gbest
23. GbestCC=PbestCC, Gbest=Pbest,
24. CC=SIpart(gbestid, idvec(gbestid))
25. Ioutbin(CC, „Malignant‟, „Benign‟);
End
The steps of the algorithm are as follows: The algorithm uses a function f6(x) that is meant for
assessing the left and right orientation of an image followed by correcting the orientation for enhancing the
classification process (Line-1). The next step of the algorithm is to carry out segmentation to ensure that no
unwanted region is selected for next process of analysis (Line-2). For this purpose, the segmentation is
carried out by obtaining the binary image using two different explicit function f7(x) and f8(x). The next part
of the algorithm is all about applying a bio-inspired algorithm in order to remove the unwanted tissue that
creates an impediment towards identifying cancerous region (Line-3 to Line-24). The algorithm obtains
histogram h, index idx, in order to obtain windows s1 and s2. The algorithm performs dual classification of
the region viz. p1 and p2 followed by computation of the threshold value T that is equivalent to s1/s2.
The algorithm further computes updated threshold followed by evaluating fitness value fit with respect to the
pbest value. Likewise, the similar check is carried out towards assessing the comparative value of pbest with
respect to gbest. This process is resumed by computing center of cluster that is considered to be the region of
best outcome for the given frame of an image. The prime agenda behind designing the optimization
technique is to filter out both pbest and gbest from the given problem space, where the fitness function is
consistently updated if there is any form of change in the dimension of the problem space. For this purpose,
if there is any form of slightest deviation for the images density (that occurs in different image samples),
6. Int J Elec & Comp Eng ISSN: 2088-8708
A novel approach to jointly address localization and classification of breast cancer using… (Sushma S. J.)
997
it can easily identity the location. However, the significant benefit is that it checks for the complete region in
order to avoid false positive while making decision. Finally, fuzzy inference system is utilized in order to
further ascertain the classification outcome for stating whether it is malignant or benign state of eh breast
cancer. The next section discusses about the outcomes obtained.
3. RESULT ANALYSIS
From the discussion of algorithm implementation, it can be seen that proposed system performs
localization as well as classification of the breast cancer from the medical dataset e.g. DDSM [36] and
MIAS [37]. Hence, the analysis of the proposed system is carried out in two discrete way viz. visual
assessment and numerical assessment. Following are the discussion of the outcomes.
(a) Input image (b) Auto segmented image (c) Primary enc
(d) Secondary enc (e) Tertiary enh (f) Localized region
Figure 2. Visual outcomes of localization of breast cancer
Figure 2 highlights the visual outcomes to show the stages of processing involved in identification
of the breast cancer. A closer look into the outcome shows that after performing involuntary segmentation of
the input image, there are three stages of bio-inspired image enhancement i.e. primary, secondary,
and tertiary enhancement Figure 2(c) to Figure 2(e). It evidently shows that each progress rendered by the
consecutive process of the proposed system entails the increase of the local contrast along with removal of
the unwanted regions. The complete processing time in order to yield this outcome is approximatey 0.26657
seconds in windows. Similarly, the proposed system also testifies the visual outcomes of the classification
process as highlighted in Table 1. The visual outcome shows that input for both normal and abnormal images
are initially assessed for any form of unwanted spaces that is not at all considered in the analysis.
Hence, after removing all the unwanted regions, the proposed system performs normalization of the images.
A closer look into this process of normalization will show that proposed system performs normalization in
quite a different manner for both normal and abnormal images. Further the process of feature extraction is
7. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 9, No. 2, April 2019 : 992 - 1001
998
carried out with an aid of decomposed wavelets obtained by applying discrete wavelet transformations.
Further applying the novel bio-inspired algorithm, the actual region of breast cancer is finally localized and is
now ready for classification. The proposed system applies rule-set based approaches using binary
classification process, where the finally localized region is declared as benign or malignant stage of cancer
after observing the normal or abnormal stages of cancer.
Table 1. Visual Outcome of Classification
Normal Abnormal
Input
Removal of unwanted region
Normalize Image
2d-DWT
After applying bio-inspired
Classification Outcomes
8. Int J Elec & Comp Eng ISSN: 2088-8708
A novel approach to jointly address localization and classification of breast cancer using… (Sushma S. J.)
999
The proposed system also performs comparative analysis to evaluate the performance of the
classificatioon The outcome of Figure 3 shows that mean and standard deviation of proposed system as well
as existing classifiers e.g. Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Artificial
Neural Network (ANN). Similarly, proposed system also offers reduced skewness and kurtosis value in
Figure 4. The classification accuracy of the proposed system is significantly high compared to existing
classifiers (in Figure 5). Apart from this, the proposed system also offers faster computational processing
time to show that it is cost-effecive approach to address the joint localization and classification of the breast
cancer with a good balance between accuracy and faster response time in Figure 6.
Figure 3. Comparative analysis of mean and
deviation
Figure 4. Comparative analysis of skewness &
kurtosis
Figure 5. Comparative analysis of accuracy Figure 6. Comparative analysis of response time
4. CONCLUSION
The proposed research work offers an insight that it is feasible to present a solution towards jointly
addressing the problems associated with detection and classification problems associated with early stage of
detection of breast cancer. The proposed system initially addresses localization problems by using a novel
multi-layer enhancement using novel threshold-based approach along with simple bio-inspired optimization
that allows its objective fncton to offer highy accurate outcome of localized region automatically. The second
part of the implementation discusses about a novel classification approach that offers significant novelty over
elimination of unwanted regions that hinders the classification of the breats cancer. Anovel bio-inspired
algorithm is implemented to ensure that it obtains both local and global outcome for ensuring highly correct
classification process. The outcome is finally utilizing rule-set system in order to perform user-friendly
inference of the critcalityof cancer in the form of benign and malignant stage.
REFERENCES
[1] Klaus D. Toennies, “Guide to Medical Image Analysis: Methods and Algorithms,” Springer, 2017.
9. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 9, No. 2, April 2019 : 992 - 1001
1000
[2] Paulo Mazzoncini de Azevedo-Marques, Arianna Mencattini, Marcello Salmeri, Rangaraj M. Rangayyan, “Medical
Image Analysis and Informatics: Computer-Aided Diagnosis and Therapy,” CRC Press, 2018.
[3] Qiang Li, Robert M. Nishikawa, “Computer-Aided Detection and Diagnosis in Medical Imaging,” Taylor & Francis,
2015.
[4] F. A. Cardillo, F. Masulli and S. Rovetta, “Automatic Approaches for CE-MRI Examination of the Breast: A
Survey,” 2017 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and
Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data
(SmartData), Exeter, 2017, pp. 147-154.
[5] M. S. Islam, N. Kaabouch and W. C. Hu, “A survey of medical imaging techniques used for breast cancer detection,”
IEEE International Conference on Electro-Information Technology, EIT 2013, Rapid City, SD, 2013, pp. 1-5.
[6] K. Gayathri and P. Raajan, “A survey of breast cancer detection based on image segmentation techniques,” 2016
International Conference on Computing Technologies and Intelligent Data Engineering (ICCTIDE'16), Kovilpatti,
2016, pp. 1-5.
[7] F. F. Ting and K. S. Sim, “Self-regulated multilayer perceptron neural network for breast cancer classification,” 2017
International Conference on Robotics, Automation and Sciences (ICORAS), Melaka, 2017, pp. 1-5.
[8] T. Amaral, S. McKenna, K. Robertson and A. Thompson, “Classification of breast-tissue microarray spots using
colour and local invariants,” 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro,
Paris, 2008, pp. 999-1002.
[9] Shahnaz, J. Hossain, S. A. Fattah, S. Ghosh and A. I. Khan, “Efficient approaches for accuracy improvement of
breast cancer classification using wisconsin database,” 2017 IEEE Region 10 Humanitarian Technology Conference
(R10-HTC), Dhaka, 2017, pp. 792-797.
[10] Sushma S J, S C Prasanna Kumar, “Advancement in Research Techniques on Medical Imaging Processing for Breast
Cancer Detection”, International Journal of Electrical and Computer Engineering (IJECE), vol. 6, no. 2, April 2016,
pp. 717-724.
[11] K. S. Beevi, M. S. Nair and G. R. Bindu, “A Multi-Classifier System for Automatic Mitosis Detection in Breast
Histopathology Images Using Deep Belief Networks,” in IEEE Journal of Translational Engineering in Health and
Medicine, vol. 5, pp. 1-11, 2017.
[12] G. Carneiro, J. Nascimento and A. P. Bradley, “Automated Analysis of Unregistered Multi-View Mammograms
With Deep Learning,” in IEEE Transactions on Medical Imaging, vol. 36, no. 11, pp. 2355-2365, Nov. 2017.
[13] F. Chokri and M. Hayet Farida, “Mammographic mass classification according to Bi-RADS lexicon,” in IET
Computer Vision, vol. 11, no. 3, pp. 189-198, 4 2017.
[14] S. Duraisamy and S. Emperumal, “Computer-aided mammogram diagnosis system using deep learning convolutional
fully complex-valued relaxation neural network classifier,” in IET Computer Vision, vol. 11, no. 8, pp. 656-662,
12 2017.
[15] A. Elmoufidi, K. El Fahssi, S. Jai-andaloussi, A. Sekkaki, Q. Gwenole and M. Lamard, “Anomaly classification in
digital mammography based on multiple-instance learning,” in IET Image Processing, vol. 12, no. 3, pp. 320-328,
3 2018.
[16] S. Manivannan, C. Cobb, S. Burgess and E. Trucco, “Subcategory Classifiers for Multiple-Instance Learning and Its
Application to Retinal Nerve Fiber Layer Visibility Classification,” in IEEE Transactions on Medical Imaging,
vol. 36, no. 5, pp. 1140-1150, May 2017.
[17] Mercan, S. Aksoy, E. Mercan, L. G. Shapiro, D. L. Weaver and J. G. Elmore, “Multi-Instance Multi-Label Learning
for Multi-Class Classification of Whole Slide Breast Histopathology Images,” in IEEE Transactions on Medical
Imaging, vol. 37, no. 1, pp. 316-325, Jan. 2018.
[18] N. I. Nizam, S. K. Alam and M. K. Hasan, “EEMD Domain AR Spectral Method for Mean Scatterer Spacing
Estimation of Breast Tumors From Ultrasound Backscattered RF Data,” in IEEE Transactions on Ultrasonics,
Ferroelectrics, and Frequency Control, vol. 64, no. 10, pp. 1487-1500, Oct. 2017.
[19] R. Rabidas, J. Chakraborty and A. Midya, “Analysis of 2D singularities for mammographic mass classification,” in
IET Computer Vision, vol. 11, no. 1, pp. 22-32, 2 2017.
[20] S. Reis et al., “Automated Classification of Breast Cancer Stroma Maturity from Histological Images,” in IEEE
Transactions on Biomedical Engineering, vol. 64, no. 10, pp. 2344-2352, Oct. 2017.
[21] M. Saha and C. Chakraborty, “Her2Net: A Deep Framework for Semantic Segmentation and Classification of Cell
Membranes and Nuclei in Breast Cancer Evaluation,” in IEEE Transactions on Image Processing, vol. 27, no. 5,
pp. 2189-2200, May 2018.
[22] Y. Song, Q. Li, H. Huang, D. Feng, M. Chen and W. Cai, “Low Dimensional Representation of Fisher Vectors for
Microscopy Image Classification,” in IEEE Transactions on Medical Imaging, vol. 36, no. 8, pp. 1636-1649,
Aug. 2017.
[23] M. Strackx, E. D'Agostino, P. Leroux and P. Reynaert, “Direct RF Subsampling Receivers Enabling Impulse-Based
UWB Signals for Breast Cancer Detection,” in IEEE Transactions on Circuits and Systems II: Express Briefs,
vol. 62, no. 2, pp. 144-148, Feb. 2015.
[24] Z. Wang, X. Xiao, H. Song, L. Wang and Q. Li, “Development of Anatomically Realistic Numerical Breast
Phantoms Based on T1- and T2-Weighted MRIs for Microwave Breast Cancer Detection,” in IEEE Antennas and
Wireless Propagation Letters, vol. 13, pp. 1757-1760, 2014.
[25] X. Wang, T. Qin, R. S. Witte and H. Xin, “Computational Feasibility Study of Contrast-Enhanced Thermoacoustic
Imaging for Breast Cancer Detection Using Realistic Numerical Breast Phantoms,” in IEEE Transactions on
Microwave Theory and Techniques, vol. 63, no. 5, pp. 1489-1501, May 2015.
10. Int J Elec & Comp Eng ISSN: 2088-8708
A novel approach to jointly address localization and classification of breast cancer using… (Sushma S. J.)
1001
[26] T. Yin, F. H. Ali and C. C. Reyes-Aldasoro, “A Robust and Artifact Resistant Algorithm of Ultrawideband Imaging
System for Breast Cancer Detection,” in IEEE Transactions on Biomedical Engineering, vol. 62, no. 6,
pp. 1514-1525, June 2015.
[27] M. D. Hossain and A. S. Mohan, “Cancer Detection in Highly Dense Breasts Using Coherently Focused Time-
Reversal Microwave Imaging,” in IEEE Transactions on Computational Imaging, vol. 3, no. 4, pp. 928-939,
Dec. 2017.
[28] M. Jalilvand, X. Li, L. Zwirello and T. Zwick, “Ultra wideband compact near-field imaging system for breast cancer
detection,” in IET Microwaves, Antennas & Propagation, vol. 9, no. 10, pp. 1009-1014, 7 16 2015.
[29] S. Kwon, H. Lee and S. Lee, “Image enhancement with Gaussian filtering in time-domain microwave imaging
system for breast cancer detection,” in Electronics Letters, vol. 52, no. 5, pp. 342-344, 3 3 2016.
[30] S. Beura, B. Majhi, R. Dash and S. Roy, “Classification of mammogram using two-dimensional discrete orthonormal
S-transform for breast cancer detection,” in Healthcare Technology Letters, vol. 2, no. 2, pp. 46-51, 4 2015.
[31] Sakthi, S. Siva, R. K. Santhi, N. Murali Krishnan, S. Ganesan, and S. Subramanian, “Wind Integrated Thermal Unit
Commitment Solution Using Grey Wolf Optimizer,” International Journal of Electrical and Computer Engineering
(IJECE) 7, no. 5 (2017): 2309-2320.
[32] Hamaine et al., “Demonstrates the proposed method precisely differentiate standard brain images from the irregular
ones and benign lesions from malignant tumors”.
[33] Bangare, Sunil L., G. Pradeepini, and Shrishailappa Tatyasaheb Patil, “Neuroendoscopy Adapter Module
Development for Better Brain Tumor Image Visualization,” International Journal of Electrical and Computer
Engineering (IJECE) 7, no. 6 (2017): 3643-3654.
[34] Sushma S J and S. C. P. Kumar, “Image Enhancement using Bio-inspired Algorithms on mammogram for cancer
detection,” International Conference on Emerging Research in Electronics, Computer Science and Technology
(ICERECT), Mandya, 2015, pp. 11-16.
[35] S. J. Sushma, S. C. Prasanna Kumar, “Multi-stage Optimization Over Extracted Feature for Detection and
Classification of Breast Cancer,” Springer-Computer Science On-line Conference, pp.276-283, 2017
[36] “DDSM: Digital Database for Screering Mammography”,
http://marathon.csee.usf.edu/Mammography/Database.html, Retrieved on 19th
April 2018
[37] “Mamographic Image Analysis Homepage”, http://www.mammoimage.org/databases/, Retrived, 13th Jan, 2017.
BIOGRAPHIES OF AUTHORS
Sushma S. J. is working as Associate Professor, Department of ECE, GSSS Institute of
Engineering and Technology for women, Mysuru. She has got 14 years of teaching experience.
She has obtained Bachelor of Engineering from Manglore University in the year 2001. In 2007.
She obtained Master of Technology from Visveswaraya Technological University, Belagavi.
Currently pursuing Ph.D. at Visveswaraya Technological University, Belagavi, India. She has
published 13 papers in national conferences 7 in international conference and 4 in international
journal. Her area of interests includes Image Processing, Computational Intelligence and
Computer Networks.
Dr Prasanna Kumar S. C. is working as Professor and Head, Department of Instrumentation
Technology, RV college of Engineering, Bangalore. He has got 18 years of teaching, 01 year of
industry and 10 years of research experience. He did his Bachelor of Engineering and master of
Engineering from Mysore University. He was awarded PhD in the year 2009 from
Avinashlingham University, Tamilnadu. He has published over 14 papers in national and
international conferences and around 24 papers in the international journals. He has received
academic excellence award for the years 2008 and 2009.