Machine learning methods have been an interesting method in the field of medical for many years, and they have achieved successful results in various fields of medical science. This paper examines the effects of using machine learning algorithms in the diagnosis and classification of breast cancer from mammography imaging data. Cancer diagnosis is the identification of images as cancer or non-cancer, and this involves image preprocessing, feature extraction, classification, and performance analysis. This article studied 93 different references mentioned in the previous years in the field of processing and tries to find an effective way to diagnose and classify breast cancer. Based on the results of this research, it can be concluded that most of today’s successful methods focus on the use of deep learning methods. Finding a new method requires an overview of existing methods in the field of deep learning methods in order to make a comparison and case study.
PREDICTION OF BREAST CANCER,COMPARATIVE REVIEW OF MACHINE LEARNING TECHNIQUES...IRJET Journal
This document presents a comparative review of machine learning techniques for breast cancer prediction. It analyzes several algorithms applied to the Wisconsin Breast Cancer Diagnosis (WBCD) dataset, including SVM, KNN, Random Forest, AdaBoost, and XGBoost. XGBoost achieved the highest accuracy of 98.24% with 0% false negatives. The study demonstrates that machine learning can effectively predict breast cancer and increase early detection accuracy.
Breast Tumor Detection Using Efficient Machine Learning and Deep Learning Tec...mlaij
Breast cancer tissues grow when cells in the breast expand and divide uncontrollably, resulting in a lump of tissue commonly called and named tumor. Breast cancer is the second most prevalent cancer among women, following skin cancer. While it is more commonly diagnosed in women aged 50 and above, it can affect individuals of any age. Although it is rare, men can also develop breast cancer, accounting for less than 1% of all cases, with approximately 2,600 cases reported annually in the United States. Early detection of breast tumors is crucial in reducing the risk of developing breast cancer. A publicly available dataset containing features of breast tumors was utilized to identify breast tumors using machine learning and deep learning techniques. Various prediction models were constructed, including logistic regression (LR), decision tree (DT), random forest (RF), support vector machine (SVM), Gradient Boosting (GB), Extreme Gradient Boosting (XGB), Light GBM, and a recurrent neural network (RNN) model. These models were trained to classify and predict breast tumor cases based on the provided features.
BREAST TUMOR DETECTION USING EFFICIENT MACHINE LEARNING AND DEEP LEARNING TEC...mlaij
This document describes a study that uses machine learning and deep learning techniques to detect breast tumors using a publicly available dataset. Various classification models were developed including logistic regression, decision trees, random forests, support vector machines, gradient boosting, extreme gradient boosting, LightGBM, and a recurrent neural network. These models were trained on features from the dataset to classify breast tumor cases as benign or malignant. The models' performance was evaluated using various metrics like accuracy, precision, recall, and F1 score. Ensemble techniques like bagging and boosting were also explored to improve the models' performance at detecting breast cancer.
Breast Tumor Detection Using Efficient Machine Learning and Deep Learning Tec...mlaij
Machine Learning and Applications: An International Journal (MLAIJ) is a quarterly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the machine learning. The journal is devoted to the publication of high quality papers on theoretical and practical aspects of machine learning and applications.The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on machine learning advancements, and establishing new collaborations in these areas. Original research papers, state-of-the-art reviews are invited for publication in all areas of machine learning.
Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of machine learning.
Breast cancer detection using machine learning approaches: a comparative studyIJECEIAES
As the cause of the breast cancer disease has not yet clearly identified and a method to prevent its occurrence has not yet been developed, its early detection has a significant role in enhancing survival rate. In fact, artificial intelligent approaches have been playing an important role to enhance the diagnosis process of breast cancer. This work has selected eight classification models that are mostly used to predict breast cancer to be under investigation. These classifiers include single and ensemble classifiers. A trusted dataset has been enhanced by applying five different feature selection methods to pick up only weighted features and to neglect others. Accordingly, a dataset of only 17 features has been developed. Based on our experimental work, three classifiers, multi-layer perceptron (MLP), support vector machine (SVM) and stack are competing with each other by attaining high classification accuracy compared to others. However, SVM is ranked on the top by obtaining an accuracy of 97.7% with classification errors of 0.029 false negative (FN) and 0.019 false positive (FP). Therefore, it is noteworthy to mention that SVM is the best classifier and it outperforms even the stack classier.
PREDICTION OF BREAST CANCER USING DATA MINING TECHNIQUESIAEME Publication
Women who have improved from breast cancer (BC) constantly panic about setback. The way that they have persevered through the meticulous treatment makes repeat their biggest fear. However, with current spreads in technology, early repeat prediction can enable patients to get treatment prior. The accessibility of broad information and propelled techniques make precise and fast prediction possible. This examination expects to think about the exactness of a couple of existing information mining calculations in predicting BC repeat. It inserts a particle swarm optimization as highlight choice into ANN classifier. An objective of increasing the accuracy level of the prediction model.
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.
Comparative analysis on bayesian classification for breast cancer problemjournalBEEI
The problem of imbalanced class distribution or small datasets is quite frequent in certain fields especially in medical domain. However, the classical Naive Bayes approach in dealing with uncertainties within medical datasets face with the difficulties in selecting prior distributions, whereby parameter estimation such as the maximum likelihood estimation (MLE) and maximum a posteriori (MAP) often hurt the accuracy of predictions. This paper presents the full Bayesian approach to assess the predictive distribution of all classes using three classifiers; naïve bayes (NB), bayesian networks (BN), and tree augmented naïve bayes (TAN) with three datasets; Breast cancer, breast cancer wisconsin, and breast tissue dataset. Next, the prediction accuracies of bayesian approaches are also compared with three standard machine learning algorithms from the literature; K-nearest neighbor (K-NN), support vector machine (SVM), and decision tree (DT). The results showed that the best performance was the bayesian networks (BN) algorithm with accuracy of 97.281%. The results are hoped to provide as base comparison for further research on breast cancer detection. All experiments are conducted in WEKA data mining tool.
PREDICTION OF BREAST CANCER,COMPARATIVE REVIEW OF MACHINE LEARNING TECHNIQUES...IRJET Journal
This document presents a comparative review of machine learning techniques for breast cancer prediction. It analyzes several algorithms applied to the Wisconsin Breast Cancer Diagnosis (WBCD) dataset, including SVM, KNN, Random Forest, AdaBoost, and XGBoost. XGBoost achieved the highest accuracy of 98.24% with 0% false negatives. The study demonstrates that machine learning can effectively predict breast cancer and increase early detection accuracy.
Breast Tumor Detection Using Efficient Machine Learning and Deep Learning Tec...mlaij
Breast cancer tissues grow when cells in the breast expand and divide uncontrollably, resulting in a lump of tissue commonly called and named tumor. Breast cancer is the second most prevalent cancer among women, following skin cancer. While it is more commonly diagnosed in women aged 50 and above, it can affect individuals of any age. Although it is rare, men can also develop breast cancer, accounting for less than 1% of all cases, with approximately 2,600 cases reported annually in the United States. Early detection of breast tumors is crucial in reducing the risk of developing breast cancer. A publicly available dataset containing features of breast tumors was utilized to identify breast tumors using machine learning and deep learning techniques. Various prediction models were constructed, including logistic regression (LR), decision tree (DT), random forest (RF), support vector machine (SVM), Gradient Boosting (GB), Extreme Gradient Boosting (XGB), Light GBM, and a recurrent neural network (RNN) model. These models were trained to classify and predict breast tumor cases based on the provided features.
BREAST TUMOR DETECTION USING EFFICIENT MACHINE LEARNING AND DEEP LEARNING TEC...mlaij
This document describes a study that uses machine learning and deep learning techniques to detect breast tumors using a publicly available dataset. Various classification models were developed including logistic regression, decision trees, random forests, support vector machines, gradient boosting, extreme gradient boosting, LightGBM, and a recurrent neural network. These models were trained on features from the dataset to classify breast tumor cases as benign or malignant. The models' performance was evaluated using various metrics like accuracy, precision, recall, and F1 score. Ensemble techniques like bagging and boosting were also explored to improve the models' performance at detecting breast cancer.
Breast Tumor Detection Using Efficient Machine Learning and Deep Learning Tec...mlaij
Machine Learning and Applications: An International Journal (MLAIJ) is a quarterly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the machine learning. The journal is devoted to the publication of high quality papers on theoretical and practical aspects of machine learning and applications.The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on machine learning advancements, and establishing new collaborations in these areas. Original research papers, state-of-the-art reviews are invited for publication in all areas of machine learning.
Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of machine learning.
Breast cancer detection using machine learning approaches: a comparative studyIJECEIAES
As the cause of the breast cancer disease has not yet clearly identified and a method to prevent its occurrence has not yet been developed, its early detection has a significant role in enhancing survival rate. In fact, artificial intelligent approaches have been playing an important role to enhance the diagnosis process of breast cancer. This work has selected eight classification models that are mostly used to predict breast cancer to be under investigation. These classifiers include single and ensemble classifiers. A trusted dataset has been enhanced by applying five different feature selection methods to pick up only weighted features and to neglect others. Accordingly, a dataset of only 17 features has been developed. Based on our experimental work, three classifiers, multi-layer perceptron (MLP), support vector machine (SVM) and stack are competing with each other by attaining high classification accuracy compared to others. However, SVM is ranked on the top by obtaining an accuracy of 97.7% with classification errors of 0.029 false negative (FN) and 0.019 false positive (FP). Therefore, it is noteworthy to mention that SVM is the best classifier and it outperforms even the stack classier.
PREDICTION OF BREAST CANCER USING DATA MINING TECHNIQUESIAEME Publication
Women who have improved from breast cancer (BC) constantly panic about setback. The way that they have persevered through the meticulous treatment makes repeat their biggest fear. However, with current spreads in technology, early repeat prediction can enable patients to get treatment prior. The accessibility of broad information and propelled techniques make precise and fast prediction possible. This examination expects to think about the exactness of a couple of existing information mining calculations in predicting BC repeat. It inserts a particle swarm optimization as highlight choice into ANN classifier. An objective of increasing the accuracy level of the prediction model.
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.
Comparative analysis on bayesian classification for breast cancer problemjournalBEEI
The problem of imbalanced class distribution or small datasets is quite frequent in certain fields especially in medical domain. However, the classical Naive Bayes approach in dealing with uncertainties within medical datasets face with the difficulties in selecting prior distributions, whereby parameter estimation such as the maximum likelihood estimation (MLE) and maximum a posteriori (MAP) often hurt the accuracy of predictions. This paper presents the full Bayesian approach to assess the predictive distribution of all classes using three classifiers; naïve bayes (NB), bayesian networks (BN), and tree augmented naïve bayes (TAN) with three datasets; Breast cancer, breast cancer wisconsin, and breast tissue dataset. Next, the prediction accuracies of bayesian approaches are also compared with three standard machine learning algorithms from the literature; K-nearest neighbor (K-NN), support vector machine (SVM), and decision tree (DT). The results showed that the best performance was the bayesian networks (BN) algorithm with accuracy of 97.281%. The results are hoped to provide as base comparison for further research on breast cancer detection. All experiments are conducted in WEKA data mining tool.
A Comprehensive Evaluation of Machine Learning Approaches for Breast Cancer C...IRJET Journal
This document compares the performance of three machine learning models - Convolutional Neural Networks (CNN), Artificial Neural Networks (ANN), and Support Vector Machines (SVM) - for classifying breast cancer from histopathology images. CNN achieved the highest classification accuracy of 87%, followed by SVM at 76% and ANN at 71%. CNN also exhibited superior sensitivity in detecting malignant cases. The document proposes using CNN, ANN, and SVM models on a breast cancer histopathology image dataset to determine which model provides the most accurate cancer classification.
This document discusses using machine learning for breast cancer detection. It begins with an introduction on breast cancer prevalence and existing machine learning methods. It then discusses breast cancer conditions in India, noting rising case numbers. Key factors in late diagnosis are identified as lack of awareness programs and low participation. The proposed methodology uses CNN for automated feature extraction to distinguish malignant from benign tumors faster. It describes preprocessing, augmentation, model testing and accuracy evaluation. Risk factors, signs, and prevention strategies are outlined. A schematic diagram and steps of the detection process are provided. The conclusion notes high accuracy achieved and potential for early detection to improve outcomes.
Toward Integrated Clinical and Gene Expression Profiles for Breast Cancer Pro...CSCJournals
Breast cancer patients with the same diagnostic and clinical prognostic profile can have markedly different clinical outcome. This difference is possibly caused by the limitation of current breast cancer prognostic indices, which group molecularly distinct patients into similar clinical classes based mainly on morphological of disease. Traditional clinical based prognosis models were discovered contain some restriction to address the heterogeneity of breast cancer. The invention of microarray technology and its ability to simultaneously interrogate thousands genes has changed the paradigm of molecular classification of human cancers as well as it shifted clinical prognosis model to broader prospect. Numerous studies have revealed the potential value of gene expression signatures in examining the risk of disease recurrence. However, currently most of these studies attempted to implement genetic marker based prognostic models to replace the traditional clinical markers, yet neglecting the rich information contain in clinical information. Therefore, this research took an effort to integrate both clinical and microarray data in order to obtain accurate breast cancer prognosis, by taking into account that these data complements each other. This article presents a review of the development of breast cancer prognosis models, concentrating precisely on clinical and gene expression profiles. The literature is reviewed in an explicit machine learning framework, which include the elements of feature selection and classification techniques.
Survival Analysis of Determinants of Breast Cancer Patients at Hossana Queen ...Premier Publishers
Breast cancer is one of the most severe diseases in the world and become the public’s ever day’s agenda in both developed and developing countries. The primary goal of this study was to identify the determinants of survival time of breast cancer patients at Hossana hospital, south Ethiopia. Kaplan-Meier estimation method and a new two-parameter probability distribution called hypertabastic are introduced to model the survival time of the data. A simulation study was carried out to evaluate the performance of the hypertabastic distribution in comparison with popular distribution with the help of R and SAS statistical software Packages. One-fourth (25%) of the total patients survived for only 2 days. 31(35.2%) were censored, and 55(62.5%) were died. Hypertabastic survival model was found to be best fitting to the breast cancer data and age, level of education, family history, breast problem before, High fat diet, child late age, early menarche, late menopause were significant risk factors for the death of breast cancer patients. Awareness has to be given for the society on causes of breast cancer and screening test and early detection policies for most risky groups has to be established.
This document describes a study that used stratified k-fold cross-validation (SKCV) on machine learning classifiers to predict cervical cancer. Four common machine learning models - support vector machine, random forest, k-nearest neighbors, and extreme gradient boosting - were applied to four cervical cancer diagnosis tests. The findings showed that a random forest classifier analyzing cervical cancer risk could assist clinicians in classifying the disease early. SKCV ensured each fold of the cervical cancer dataset contained the same proportion of observations for each label, improving on standard k-fold cross-validation for this classification problem with imbalanced classes.
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.
USING DATA MINING TECHNIQUES FOR DIAGNOSIS AND PROGNOSIS OF CANCER DISEASEIJCSEIT Journal
Breast cancer is one of the leading cancers for women in developed countries including India. It is the
second most common cause of cancer death in women. The high incidence of breast cancer in women has
increased significantly in the last years. In this paper we have discussed various data mining approaches
that have been utilized for breast cancer diagnosis and prognosis. Breast Cancer Diagnosis is
distinguishing of benign from malignant breast lumps and Breast Cancer Prognosis predicts when Breast
Cancer is to recur in patients that have had their cancers excised. This study paper summarizes various
review and technical articles on breast cancer diagnosis and prognosis also we focus on current research
being carried out using the data mining techniques to enhance the breast cancer diagnosis and prognosis.
A Comparative Analysis of Hybridized Genetic Algorithm in Predictive Models o...Shakas Technologies
A Comparative Analysis of Hybridized Genetic Algorithm in Predictive Models of Breast Cancer Tumors.
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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.
Performance Evaluation using Supervised Learning Algorithms for Breast Cancer...IRJET Journal
The document discusses using supervised machine learning algorithms to evaluate the performance of breast cancer diagnosis. It evaluates algorithms like perceptron, cascade-forward backpropagation, and feed-forward backpropagation on a breast cancer dataset from the Wisconsin Breast Cancer Diagnosis database. The algorithms are used to develop a process for diagnosis and prediction of breast cancer that could help physicians diagnose the disease more accurately.
IRJET - Classifying Breast Cancer Tumour Type using Convolution Neural Netwo...IRJET Journal
This document presents a study that uses a convolutional neural network (CNN) deep learning model to classify breast cancer tumors as benign or malignant based on ultrasonic images. The researchers trained a CNN model using a dataset of ultrasonic breast images labeled as benign or malignant. The trained model can then analyze new ultrasonic images and determine the tumor type, which could help doctors diagnose and treat breast cancer more accurately. The document provides background on breast cancer and existing diagnosis methods, describes the proposed CNN classification system, and reviews related work applying machine learning to breast cancer analysis.
This document presents a proposal for using machine learning to predict cancer quickly and accurately. It discusses how both machine learning and deep learning have been used by scientists to predict cancer by analyzing large amounts of healthcare data. The authors propose a new framework that uses basic machine learning algorithms to achieve high accuracy in cancer classification while reducing the size of the required dataset and enhancing prognostic prediction with less input data. The goal is to develop a fast, accurate, and scalable system for cancer prediction.
A KNOWLEDGE DISCOVERY APPROACH FOR BREAST CANCER MANAGEMENT IN THE KINGDOM OF...hiij
In this paper, we introduce an approach to improve and support decision-making process for breast cancer management in the Kingdom of Saudi Arabia. This can be accomplished by applying different association rule mining algorithms on the cancer information system in Saudi Arabia. It also provides valuable information about predicted distribution and segmentation of cancer in Saudi Arabia, which may be linked to possible risk factors. From the extracted patterns, the information need to be considered in the decision making process can be identified and recognized as well, which yields to knowledge based decisions. Consequently, identifying health risk behaviors among target group of patients and adopting intervention and preventive measures can be initiated in order to decrease breast cancer incidence and prevalence and ultimately the health care costs.
Image processing and machine learning techniques used in computer-aided dete...IJECEIAES
This paper aims to review the previously developed Computer-aided detection (CAD) systems for mammogram screening because increasing death rate in women due to breast cancer is a global medical issue and it can be controlled only by early detection with regular screening. Till now mammography is the widely used breast imaging modality. CAD systems have been adopted by the radiologists to increase the accuracy of the breast cancer diagnosis by avoiding human errors and experience related issues. This study reveals that in spite of the higher accuracy obtained by the earlier proposed CAD systems for breast cancer diagnosis, they are not fully automated. Moreover, the false-positive mammogram screening cases are high in number and over-diagnosis of breast cancer exposes a patient towards harmful overtreatment for which a huge amount of money is being wasted. In addition, it is also reported that the mammogram screening result with and without CAD systems does not have noticeable difference, whereas the undetected cancer cases by CAD system are increasing. Thus, future research is required to improve the performance of CAD system for mammogram screening and make it completely automated.
Applying Deep Learning to Transform Breast Cancer DiagnosisCognizant
Deep convolutional neural networks can assist pathologists in breast cancer diagnosis by automatically filtering benign tissue biopsies, identifying malignant regions and labeling important cellular features like nuclei for further analysis. Automatic detection of diagnostically relevant regions-of-interest and nuclei segmentation reduces the pathologist’s workload, while ensuring that no critical region is overlooked, rendering breast cancer diagnosis more reliable, efficient and cost-effective.
GRAPHICAL MODEL AND CLUSTERINGREGRESSION BASED METHODS FOR CAUSAL INTERACTION...ijaia
The early detection of Breast Cancer, the deadly disease that mostly affects women is extremely complex because it requires various features of the cell type. Therefore, the efficient approach to diagnosing Breast Cancer at the early stage was to apply artificial intelligence where machines are simulated with intelligence and programmed to think and act like a human. This allows machines to passively learn and find a pattern, which can be used later to detect any new changes that may occur. In general, machine learning is quite useful particularly in the medical field, which depends on complex genomic measurements such as microarray technique and would increase the accuracy and precision of results. With this technology, doctors can easily diagnose patients with cancer quickly and apply the proper treatment in a timely manner. Therefore, the goal of this paper is to address and propose a robust Breast Cancer diagnostic system using complex genomic analysis via microarray technology. The system will combine two machine learning methods, K-means cluster, and linear regression.
Graphical Model and Clustering-Regression based Methods for Causal Interactio...gerogepatton
The early detection of Breast Cancer, the deadly disease that mostly affects women is extremely complex
because it requires various features of the cell type. Therefore, the efficient approach to diagnosing Breast
Cancer at the early stage was to apply artificial intelligence where machines are simulated with
intelligence and programmed to think and act like a human. This allows machines to passively learn and
find a pattern, which can be used later to detect any new changes that may occur. In general, machine
learning is quite useful particularly in the medical field, which depends on complex genomic
measurements such as microarray technique and would increase the accuracy and precision of results.
With this technology, doctors can easily diagnose patients with cancer quickly and apply the proper
treatment in a timely manner. Therefore, the goal of this paper is to address and propose a robust Breast
Cancer diagnostic system using complex genomic analysis via microarray technology. The system will
combine two machine learning methods, K-means cluster, and linear regression.
GRAPHICAL MODEL AND CLUSTERINGREGRESSION BASED METHODS FOR CAUSAL INTERACTION...gerogepatton
The early detection of Breast Cancer, the deadly disease that mostly affects women is extremely complex because it requires various features of the cell type. Therefore, the efficient approach to diagnosing Breast Cancer at the early stage was to apply artificial intelligence where machines are simulated with intelligence and programmed to think and act like a human. This allows machines to passively learn and find a pattern, which can be used later to detect any new changes that may occur. In general, machine learning is quite useful particularly in the medical field, which depends on complex genomic measurements such as microarray technique and would increase the accuracy and precision of results. With this technology, doctors can easily diagnose patients with cancer quickly and apply the proper treatment in a timely manner. Therefore, the goal of this paper is to address and propose a robust Breast Cancer diagnostic system using complex genomic analysis via microarray technology. The system will combine two machine learning methods, K-means cluster, and linear regression.
A KNOWLEDGE DISCOVERY APPROACH FOR BREAST CANCER MANAGEMENT IN THE KINGDOM OF...hiij
In this paper, we introduce an approach to improve and support decision-making process for breast cancer management in the Kingdom of Saudi Arabia. This can be accomplished by applying different association rule mining algorithms on the cancer information system in Saudi Arabia. It also provides valuable information about predicted distribution and segmentation of cancer in Saudi Arabia, which may be linked to possible risk factors. From the extracted patterns, the information need to be considered in the decision making process can be identified and recognized as well, which yields to knowledge based decisions.Consequently, identifying health risk behaviors among target group of patients and adopting interventional and preventive measures can be initiated in order to decrease breast cancer incidence and prevalence and ultimately the health care costs
A Comparative Study on the Methods Used for the Detection of Breast Cancerrahulmonikasharma
Among women in the world, the death caused by the Breast cancer has become the leading role. At an initial stage, the tumor in the breast is hard to detect. Manual attempt have proven to be time consuming and inefficient in many cases. Hence there is a need for efficient methods that diagnoses the cancerous cell without human involvement with high accuracy. Mammography is a special case of CT scan which adopts X-ray method with high resolution film. so that it can detect well the tumors in the breast. This paper describes the comparative study of the various data mining methods on the detection of the breast cancer by using image processing techniques.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
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A Comprehensive Evaluation of Machine Learning Approaches for Breast Cancer C...IRJET Journal
This document compares the performance of three machine learning models - Convolutional Neural Networks (CNN), Artificial Neural Networks (ANN), and Support Vector Machines (SVM) - for classifying breast cancer from histopathology images. CNN achieved the highest classification accuracy of 87%, followed by SVM at 76% and ANN at 71%. CNN also exhibited superior sensitivity in detecting malignant cases. The document proposes using CNN, ANN, and SVM models on a breast cancer histopathology image dataset to determine which model provides the most accurate cancer classification.
This document discusses using machine learning for breast cancer detection. It begins with an introduction on breast cancer prevalence and existing machine learning methods. It then discusses breast cancer conditions in India, noting rising case numbers. Key factors in late diagnosis are identified as lack of awareness programs and low participation. The proposed methodology uses CNN for automated feature extraction to distinguish malignant from benign tumors faster. It describes preprocessing, augmentation, model testing and accuracy evaluation. Risk factors, signs, and prevention strategies are outlined. A schematic diagram and steps of the detection process are provided. The conclusion notes high accuracy achieved and potential for early detection to improve outcomes.
Toward Integrated Clinical and Gene Expression Profiles for Breast Cancer Pro...CSCJournals
Breast cancer patients with the same diagnostic and clinical prognostic profile can have markedly different clinical outcome. This difference is possibly caused by the limitation of current breast cancer prognostic indices, which group molecularly distinct patients into similar clinical classes based mainly on morphological of disease. Traditional clinical based prognosis models were discovered contain some restriction to address the heterogeneity of breast cancer. The invention of microarray technology and its ability to simultaneously interrogate thousands genes has changed the paradigm of molecular classification of human cancers as well as it shifted clinical prognosis model to broader prospect. Numerous studies have revealed the potential value of gene expression signatures in examining the risk of disease recurrence. However, currently most of these studies attempted to implement genetic marker based prognostic models to replace the traditional clinical markers, yet neglecting the rich information contain in clinical information. Therefore, this research took an effort to integrate both clinical and microarray data in order to obtain accurate breast cancer prognosis, by taking into account that these data complements each other. This article presents a review of the development of breast cancer prognosis models, concentrating precisely on clinical and gene expression profiles. The literature is reviewed in an explicit machine learning framework, which include the elements of feature selection and classification techniques.
Survival Analysis of Determinants of Breast Cancer Patients at Hossana Queen ...Premier Publishers
Breast cancer is one of the most severe diseases in the world and become the public’s ever day’s agenda in both developed and developing countries. The primary goal of this study was to identify the determinants of survival time of breast cancer patients at Hossana hospital, south Ethiopia. Kaplan-Meier estimation method and a new two-parameter probability distribution called hypertabastic are introduced to model the survival time of the data. A simulation study was carried out to evaluate the performance of the hypertabastic distribution in comparison with popular distribution with the help of R and SAS statistical software Packages. One-fourth (25%) of the total patients survived for only 2 days. 31(35.2%) were censored, and 55(62.5%) were died. Hypertabastic survival model was found to be best fitting to the breast cancer data and age, level of education, family history, breast problem before, High fat diet, child late age, early menarche, late menopause were significant risk factors for the death of breast cancer patients. Awareness has to be given for the society on causes of breast cancer and screening test and early detection policies for most risky groups has to be established.
This document describes a study that used stratified k-fold cross-validation (SKCV) on machine learning classifiers to predict cervical cancer. Four common machine learning models - support vector machine, random forest, k-nearest neighbors, and extreme gradient boosting - were applied to four cervical cancer diagnosis tests. The findings showed that a random forest classifier analyzing cervical cancer risk could assist clinicians in classifying the disease early. SKCV ensured each fold of the cervical cancer dataset contained the same proportion of observations for each label, improving on standard k-fold cross-validation for this classification problem with imbalanced classes.
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.
USING DATA MINING TECHNIQUES FOR DIAGNOSIS AND PROGNOSIS OF CANCER DISEASEIJCSEIT Journal
Breast cancer is one of the leading cancers for women in developed countries including India. It is the
second most common cause of cancer death in women. The high incidence of breast cancer in women has
increased significantly in the last years. In this paper we have discussed various data mining approaches
that have been utilized for breast cancer diagnosis and prognosis. Breast Cancer Diagnosis is
distinguishing of benign from malignant breast lumps and Breast Cancer Prognosis predicts when Breast
Cancer is to recur in patients that have had their cancers excised. This study paper summarizes various
review and technical articles on breast cancer diagnosis and prognosis also we focus on current research
being carried out using the data mining techniques to enhance the breast cancer diagnosis and prognosis.
A Comparative Analysis of Hybridized Genetic Algorithm in Predictive Models o...Shakas Technologies
A Comparative Analysis of Hybridized Genetic Algorithm in Predictive Models of Breast Cancer Tumors.
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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.
Performance Evaluation using Supervised Learning Algorithms for Breast Cancer...IRJET Journal
The document discusses using supervised machine learning algorithms to evaluate the performance of breast cancer diagnosis. It evaluates algorithms like perceptron, cascade-forward backpropagation, and feed-forward backpropagation on a breast cancer dataset from the Wisconsin Breast Cancer Diagnosis database. The algorithms are used to develop a process for diagnosis and prediction of breast cancer that could help physicians diagnose the disease more accurately.
IRJET - Classifying Breast Cancer Tumour Type using Convolution Neural Netwo...IRJET Journal
This document presents a study that uses a convolutional neural network (CNN) deep learning model to classify breast cancer tumors as benign or malignant based on ultrasonic images. The researchers trained a CNN model using a dataset of ultrasonic breast images labeled as benign or malignant. The trained model can then analyze new ultrasonic images and determine the tumor type, which could help doctors diagnose and treat breast cancer more accurately. The document provides background on breast cancer and existing diagnosis methods, describes the proposed CNN classification system, and reviews related work applying machine learning to breast cancer analysis.
This document presents a proposal for using machine learning to predict cancer quickly and accurately. It discusses how both machine learning and deep learning have been used by scientists to predict cancer by analyzing large amounts of healthcare data. The authors propose a new framework that uses basic machine learning algorithms to achieve high accuracy in cancer classification while reducing the size of the required dataset and enhancing prognostic prediction with less input data. The goal is to develop a fast, accurate, and scalable system for cancer prediction.
A KNOWLEDGE DISCOVERY APPROACH FOR BREAST CANCER MANAGEMENT IN THE KINGDOM OF...hiij
In this paper, we introduce an approach to improve and support decision-making process for breast cancer management in the Kingdom of Saudi Arabia. This can be accomplished by applying different association rule mining algorithms on the cancer information system in Saudi Arabia. It also provides valuable information about predicted distribution and segmentation of cancer in Saudi Arabia, which may be linked to possible risk factors. From the extracted patterns, the information need to be considered in the decision making process can be identified and recognized as well, which yields to knowledge based decisions. Consequently, identifying health risk behaviors among target group of patients and adopting intervention and preventive measures can be initiated in order to decrease breast cancer incidence and prevalence and ultimately the health care costs.
Image processing and machine learning techniques used in computer-aided dete...IJECEIAES
This paper aims to review the previously developed Computer-aided detection (CAD) systems for mammogram screening because increasing death rate in women due to breast cancer is a global medical issue and it can be controlled only by early detection with regular screening. Till now mammography is the widely used breast imaging modality. CAD systems have been adopted by the radiologists to increase the accuracy of the breast cancer diagnosis by avoiding human errors and experience related issues. This study reveals that in spite of the higher accuracy obtained by the earlier proposed CAD systems for breast cancer diagnosis, they are not fully automated. Moreover, the false-positive mammogram screening cases are high in number and over-diagnosis of breast cancer exposes a patient towards harmful overtreatment for which a huge amount of money is being wasted. In addition, it is also reported that the mammogram screening result with and without CAD systems does not have noticeable difference, whereas the undetected cancer cases by CAD system are increasing. Thus, future research is required to improve the performance of CAD system for mammogram screening and make it completely automated.
Applying Deep Learning to Transform Breast Cancer DiagnosisCognizant
Deep convolutional neural networks can assist pathologists in breast cancer diagnosis by automatically filtering benign tissue biopsies, identifying malignant regions and labeling important cellular features like nuclei for further analysis. Automatic detection of diagnostically relevant regions-of-interest and nuclei segmentation reduces the pathologist’s workload, while ensuring that no critical region is overlooked, rendering breast cancer diagnosis more reliable, efficient and cost-effective.
GRAPHICAL MODEL AND CLUSTERINGREGRESSION BASED METHODS FOR CAUSAL INTERACTION...ijaia
The early detection of Breast Cancer, the deadly disease that mostly affects women is extremely complex because it requires various features of the cell type. Therefore, the efficient approach to diagnosing Breast Cancer at the early stage was to apply artificial intelligence where machines are simulated with intelligence and programmed to think and act like a human. This allows machines to passively learn and find a pattern, which can be used later to detect any new changes that may occur. In general, machine learning is quite useful particularly in the medical field, which depends on complex genomic measurements such as microarray technique and would increase the accuracy and precision of results. With this technology, doctors can easily diagnose patients with cancer quickly and apply the proper treatment in a timely manner. Therefore, the goal of this paper is to address and propose a robust Breast Cancer diagnostic system using complex genomic analysis via microarray technology. The system will combine two machine learning methods, K-means cluster, and linear regression.
Graphical Model and Clustering-Regression based Methods for Causal Interactio...gerogepatton
The early detection of Breast Cancer, the deadly disease that mostly affects women is extremely complex
because it requires various features of the cell type. Therefore, the efficient approach to diagnosing Breast
Cancer at the early stage was to apply artificial intelligence where machines are simulated with
intelligence and programmed to think and act like a human. This allows machines to passively learn and
find a pattern, which can be used later to detect any new changes that may occur. In general, machine
learning is quite useful particularly in the medical field, which depends on complex genomic
measurements such as microarray technique and would increase the accuracy and precision of results.
With this technology, doctors can easily diagnose patients with cancer quickly and apply the proper
treatment in a timely manner. Therefore, the goal of this paper is to address and propose a robust Breast
Cancer diagnostic system using complex genomic analysis via microarray technology. The system will
combine two machine learning methods, K-means cluster, and linear regression.
GRAPHICAL MODEL AND CLUSTERINGREGRESSION BASED METHODS FOR CAUSAL INTERACTION...gerogepatton
The early detection of Breast Cancer, the deadly disease that mostly affects women is extremely complex because it requires various features of the cell type. Therefore, the efficient approach to diagnosing Breast Cancer at the early stage was to apply artificial intelligence where machines are simulated with intelligence and programmed to think and act like a human. This allows machines to passively learn and find a pattern, which can be used later to detect any new changes that may occur. In general, machine learning is quite useful particularly in the medical field, which depends on complex genomic measurements such as microarray technique and would increase the accuracy and precision of results. With this technology, doctors can easily diagnose patients with cancer quickly and apply the proper treatment in a timely manner. Therefore, the goal of this paper is to address and propose a robust Breast Cancer diagnostic system using complex genomic analysis via microarray technology. The system will combine two machine learning methods, K-means cluster, and linear regression.
A KNOWLEDGE DISCOVERY APPROACH FOR BREAST CANCER MANAGEMENT IN THE KINGDOM OF...hiij
In this paper, we introduce an approach to improve and support decision-making process for breast cancer management in the Kingdom of Saudi Arabia. This can be accomplished by applying different association rule mining algorithms on the cancer information system in Saudi Arabia. It also provides valuable information about predicted distribution and segmentation of cancer in Saudi Arabia, which may be linked to possible risk factors. From the extracted patterns, the information need to be considered in the decision making process can be identified and recognized as well, which yields to knowledge based decisions.Consequently, identifying health risk behaviors among target group of patients and adopting interventional and preventive measures can be initiated in order to decrease breast cancer incidence and prevalence and ultimately the health care costs
A Comparative Study on the Methods Used for the Detection of Breast Cancerrahulmonikasharma
Among women in the world, the death caused by the Breast cancer has become the leading role. At an initial stage, the tumor in the breast is hard to detect. Manual attempt have proven to be time consuming and inefficient in many cases. Hence there is a need for efficient methods that diagnoses the cancerous cell without human involvement with high accuracy. Mammography is a special case of CT scan which adopts X-ray method with high resolution film. so that it can detect well the tumors in the breast. This paper describes the comparative study of the various data mining methods on the detection of the breast cancer by using image processing techniques.
Similar to Breast cancer diagnosis: a survey of pre-processing, segmentation, feature extraction and classification (20)
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Neural network optimizer of proportional-integral-differential controller par...IJECEIAES
Wide application of proportional-integral-differential (PID)-regulator in industry requires constant improvement of methods of its parameters adjustment. The paper deals with the issues of optimization of PID-regulator parameters with the use of neural network technology methods. A methodology for choosing the architecture (structure) of neural network optimizer is proposed, which consists in determining the number of layers, the number of neurons in each layer, as well as the form and type of activation function. Algorithms of neural network training based on the application of the method of minimizing the mismatch between the regulated value and the target value are developed. The method of back propagation of gradients is proposed to select the optimal training rate of neurons of the neural network. The neural network optimizer, which is a superstructure of the linear PID controller, allows increasing the regulation accuracy from 0.23 to 0.09, thus reducing the power consumption from 65% to 53%. The results of the conducted experiments allow us to conclude that the created neural superstructure may well become a prototype of an automatic voltage regulator (AVR)-type industrial controller for tuning the parameters of the PID controller.
An improved modulation technique suitable for a three level flying capacitor ...IJECEIAES
This research paper introduces an innovative modulation technique for controlling a 3-level flying capacitor multilevel inverter (FCMLI), aiming to streamline the modulation process in contrast to conventional methods. The proposed
simplified modulation technique paves the way for more straightforward and
efficient control of multilevel inverters, enabling their widespread adoption and
integration into modern power electronic systems. Through the amalgamation of
sinusoidal pulse width modulation (SPWM) with a high-frequency square wave
pulse, this controlling technique attains energy equilibrium across the coupling
capacitor. The modulation scheme incorporates a simplified switching pattern
and a decreased count of voltage references, thereby simplifying the control
algorithm.
A review on features and methods of potential fishing zoneIJECEIAES
This review focuses on the importance of identifying potential fishing zones in seawater for sustainable fishing practices. It explores features like sea surface temperature (SST) and sea surface height (SSH), along with classification methods such as classifiers. The features like SST, SSH, and different classifiers used to classify the data, have been figured out in this review study. This study underscores the importance of examining potential fishing zones using advanced analytical techniques. It thoroughly explores the methodologies employed by researchers, covering both past and current approaches. The examination centers on data characteristics and the application of classification algorithms for classification of potential fishing zones. Furthermore, the prediction of potential fishing zones relies significantly on the effectiveness of classification algorithms. Previous research has assessed the performance of models like support vector machines, naïve Bayes, and artificial neural networks (ANN). In the previous result, the results of support vector machine (SVM) were 97.6% more accurate than naive Bayes's 94.2% to classify test data for fisheries classification. By considering the recent works in this area, several recommendations for future works are presented to further improve the performance of the potential fishing zone models, which is important to the fisheries community.
Electrical signal interference minimization using appropriate core material f...IJECEIAES
As demand for smaller, quicker, and more powerful devices rises, Moore's law is strictly followed. The industry has worked hard to make little devices that boost productivity. The goal is to optimize device density. Scientists are reducing connection delays to improve circuit performance. This helped them understand three-dimensional integrated circuit (3D IC) concepts, which stack active devices and create vertical connections to diminish latency and lower interconnects. Electrical involvement is a big worry with 3D integrates circuits. Researchers have developed and tested through silicon via (TSV) and substrates to decrease electrical wave involvement. This study illustrates a novel noise coupling reduction method using several electrical involvement models. A 22% drop in electrical involvement from wave-carrying to victim TSVs introduces this new paradigm and improves system performance even at higher THz frequencies.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
Enhancing battery system identification: nonlinear autoregressive modeling fo...IJECEIAES
Precisely characterizing Li-ion batteries is essential for optimizing their
performance, enhancing safety, and prolonging their lifespan across various
applications, such as electric vehicles and renewable energy systems. This
article introduces an innovative nonlinear methodology for system
identification of a Li-ion battery, employing a nonlinear autoregressive with
exogenous inputs (NARX) model. The proposed approach integrates the
benefits of nonlinear modeling with the adaptability of the NARX structure,
facilitating a more comprehensive representation of the intricate
electrochemical processes within the battery. Experimental data collected
from a Li-ion battery operating under diverse scenarios are employed to
validate the effectiveness of the proposed methodology. The identified
NARX model exhibits superior accuracy in predicting the battery's behavior
compared to traditional linear models. This study underscores the
importance of accounting for nonlinearities in battery modeling, providing
insights into the intricate relationships between state-of-charge, voltage, and
current under dynamic conditions.
Smart grid deployment: from a bibliometric analysis to a surveyIJECEIAES
Smart grids are one of the last decades' innovations in electrical energy.
They bring relevant advantages compared to the traditional grid and
significant interest from the research community. Assessing the field's
evolution is essential to propose guidelines for facing new and future smart
grid challenges. In addition, knowing the main technologies involved in the
deployment of smart grids (SGs) is important to highlight possible
shortcomings that can be mitigated by developing new tools. This paper
contributes to the research trends mentioned above by focusing on two
objectives. First, a bibliometric analysis is presented to give an overview of
the current research level about smart grid deployment. Second, a survey of
the main technological approaches used for smart grid implementation and
their contributions are highlighted. To that effect, we searched the Web of
Science (WoS), and the Scopus databases. We obtained 5,663 documents
from WoS and 7,215 from Scopus on smart grid implementation or
deployment. With the extraction limitation in the Scopus database, 5,872 of
the 7,215 documents were extracted using a multi-step process. These two
datasets have been analyzed using a bibliometric tool called bibliometrix.
The main outputs are presented with some recommendations for future
research.
Use of analytical hierarchy process for selecting and prioritizing islanding ...IJECEIAES
One of the problems that are associated to power systems is islanding
condition, which must be rapidly and properly detected to prevent any
negative consequences on the system's protection, stability, and security.
This paper offers a thorough overview of several islanding detection
strategies, which are divided into two categories: classic approaches,
including local and remote approaches, and modern techniques, including
techniques based on signal processing and computational intelligence.
Additionally, each approach is compared and assessed based on several
factors, including implementation costs, non-detected zones, declining
power quality, and response times using the analytical hierarchy process
(AHP). The multi-criteria decision-making analysis shows that the overall
weight of passive methods (24.7%), active methods (7.8%), hybrid methods
(5.6%), remote methods (14.5%), signal processing-based methods (26.6%),
and computational intelligent-based methods (20.8%) based on the
comparison of all criteria together. Thus, it can be seen from the total weight
that hybrid approaches are the least suitable to be chosen, while signal
processing-based methods are the most appropriate islanding detection
method to be selected and implemented in power system with respect to the
aforementioned factors. Using Expert Choice software, the proposed
hierarchy model is studied and examined.
Enhancing of single-stage grid-connected photovoltaic system using fuzzy logi...IJECEIAES
The power generated by photovoltaic (PV) systems is influenced by
environmental factors. This variability hampers the control and utilization of
solar cells' peak output. In this study, a single-stage grid-connected PV
system is designed to enhance power quality. Our approach employs fuzzy
logic in the direct power control (DPC) of a three-phase voltage source
inverter (VSI), enabling seamless integration of the PV connected to the
grid. Additionally, a fuzzy logic-based maximum power point tracking
(MPPT) controller is adopted, which outperforms traditional methods like
incremental conductance (INC) in enhancing solar cell efficiency and
minimizing the response time. Moreover, the inverter's real-time active and
reactive power is directly managed to achieve a unity power factor (UPF).
The system's performance is assessed through MATLAB/Simulink
implementation, showing marked improvement over conventional methods,
particularly in steady-state and varying weather conditions. For solar
irradiances of 500 and 1,000 W/m2
, the results show that the proposed
method reduces the total harmonic distortion (THD) of the injected current
to the grid by approximately 46% and 38% compared to conventional
methods, respectively. Furthermore, we compare the simulation results with
IEEE standards to evaluate the system's grid compatibility.
Enhancing photovoltaic system maximum power point tracking with fuzzy logic-b...IJECEIAES
Photovoltaic systems have emerged as a promising energy resource that
caters to the future needs of society, owing to their renewable, inexhaustible,
and cost-free nature. The power output of these systems relies on solar cell
radiation and temperature. In order to mitigate the dependence on
atmospheric conditions and enhance power tracking, a conventional
approach has been improved by integrating various methods. To optimize
the generation of electricity from solar systems, the maximum power point
tracking (MPPT) technique is employed. To overcome limitations such as
steady-state voltage oscillations and improve transient response, two
traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb
and observe (P&O), have been modified. This research paper aims to
simulate and validate the step size of the proposed modified P&O and FLC
techniques within the MPPT algorithm using MATLAB/Simulink for
efficient power tracking in photovoltaic systems.
Adaptive synchronous sliding control for a robot manipulator based on neural ...IJECEIAES
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
robot hands is always an attractive topic in the research community. This is a
challenging problem because robot manipulators are complex nonlinear systems
and are often subject to fluctuations in loads and external disturbances. This
article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller
ensures that the positions of the joints track the desired trajectory, synchronize
the errors, and significantly reduces chattering. First, the synchronous tracking
errors and synchronous sliding surfaces are presented. Second, the synchronous
tracking error dynamics are determined. Third, a robust adaptive control law is
designed,the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy
logic. The built algorithm ensures that the tracking and approximation errors
are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results.
Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is
significantly reduced.
Remote field-programmable gate array laboratory for signal acquisition and de...IJECEIAES
A remote laboratory utilizing field-programmable gate array (FPGA) technologies enhances students’ learning experience anywhere and anytime in embedded system design. Existing remote laboratories prioritize hardware access and visual feedback for observing board behavior after programming, neglecting comprehensive debugging tools to resolve errors that require internal signal acquisition. This paper proposes a novel remote embeddedsystem design approach targeting FPGA technologies that are fully interactive via a web-based platform. Our solution provides FPGA board access and debugging capabilities beyond the visual feedback provided by existing remote laboratories. We implemented a lab module that allows users to seamlessly incorporate into their FPGA design. The module minimizes hardware resource utilization while enabling the acquisition of a large number of data samples from the signal during the experiments by adaptively compressing the signal prior to data transmission. The results demonstrate an average compression ratio of 2.90 across three benchmark signals, indicating efficient signal acquisition and effective debugging and analysis. This method allows users to acquire more data samples than conventional methods. The proposed lab allows students to remotely test and debug their designs, bridging the gap between theory and practice in embedded system design.
Detecting and resolving feature envy through automated machine learning and m...IJECEIAES
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.
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Breast cancer diagnosis: a survey of pre-processing, segmentation, feature extraction and classification
1. International Journal of Electrical and Computer Engineering (IJECE)
Vol. 12, No. 6, December 2022, pp. 6397~6409
ISSN: 2088-8708, DOI: 10.11591/ijece.v12i6.pp6397-6409 6397
Journal homepage: http://ijece.iaescore.com
Breast cancer diagnosis: a survey of pre-processing,
segmentation, feature extraction and classification
Ehsan Sadeghi Pour, Mahdi Esmaeili, Morteza Romoozi
Department of Electrical and Computer Engineering, Kashan Branch, Islamic Azad University, Kashan, Iran
Article Info ABSTRACT
Article history:
Received Oct 13, 2021
Revised Jun 4, 2022
Accepted Jul 1, 2022
Machine learning methods have been an interesting method in the field of
medical for many years, and they have achieved successful results in various
fields of medical science. This paper examines the effects of using machine
learning algorithms in the diagnosis and classification of breast cancer from
mammography imaging data. Cancer diagnosis is the identification of
images as cancer or non-cancer, and this involves image preprocessing,
feature extraction, classification, and performance analysis. This article
studied 93 different references mentioned in the previous years in the field
of processing and tries to find an effective way to diagnose and classify
breast cancer. Based on the results of this research, it can be concluded that
most of today’s successful methods focus on the use of deep learning
methods. Finding a new method requires an overview of existing methods in
the field of deep learning methods in order to make a comparison and case
study.
Keywords:
Breast cancer classification
Breast cancer diagnosis
Deep learning
Machine learning
Mammography
This is an open access article under the CC BY-SA license.
Corresponding Author:
Mahdi Esmaeili
Department of Electrical and Computer Engineering, Kashan Branch, Islamic Azad University
Ostadan st., Qotb-e Ravandi st., Kashan, Isfahan, Iran
Email: M.esmaeili@iaukashan.ac.ir
1. INTRODUCTION
Breast cancer is one of the most serious and prevalent types of cancer, affecting the majority of
women and being the main cause of death. Between 1 in 8 and 1 in 12 women in today’s developed world
will acquire breast cancer during their lifetime [1]. It is critical to forecast breast cancer risk in order to
combat the disease. Breast cancer risk is classified into two categories [1]. The first type implies that a person
may get breast cancer within a specified time period [1]. The second kind implies the probability of a
high-risk gene mutation [2]. Breast tumors are an abnormal growth of breast tissue that may manifest as
discharge from a lump or nipple or a change in the texture of the skin around the nipple area. Cancers are
uncontrolled cell divisions that are capable of invading other tissues. Through the blood and lymphatic
systems, cancer cells can move to different areas of the body [3]. This is the greatest cause of death among
women in their forties and fifties [3]. Breast cancer is the second most frequent type of cancer and the main
cause of cancer death in women, trailing only lung cancer [3]. In the previous 50 years, the disease has grown
in prominence, and its prevalence has increased in recent years. Breast cancer screening guidelines are scarce
at the moment. The Iranian preventive services working group recommends screening for women between
the ages of 45 and 70 but provides no definitive recommendations for women older than this age range.
Breast cancer risk prediction can be used to both incentivize high-risk women who are not already screened
and ensure that screening criteria are followed by those who would not do so otherwise. A statistical model
that predicts the risk of breast cancer can also be utilized to build cancer preventive and risk reduction
measures [4]. As such, the goal of this project is to construct models capable of properly forecasting
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women’s chance of developing breast cancer over the next five years. As a result, it is vital to discern
between an accurate diagnosis and a correct decision when viewing mammography images.
Numerous previous studies have used the Gail model to estimate the risk of breast cancer. The Gail
model is a statistical model that estimates the risk of breast cancer in women with no personal history of the
disease and no known mutations in breast cancer-risk genes [5]. Historically, this model required six inputs:
current age, menarche age, birth age, the number of first-degree relatives diagnosed with breast cancer,
race/ethnic origin, and the number of previous breast biopsies [5]. The Gail model combines both generic and
specific breast cancer risk rates, and these inputs (weighted using logistic regression) are used to anticipate a
woman’s likelihood of developing breast cancer [6]. Breast cancer risk assessment tool (BCRAT) is Gail
model implementations that, when available, will take into account both the six classic Gail model inputs and
atypical hyperplasia personal data history [7]. On the other hand, the Gail model is far from ideal. This
strategy has been demonstrated to be unsuccessful in certain parts of the population for forecasting breast
cancer risk [8], [9]. Furthermore, this method has difficulty differentiating breast cancer from non-breast
cancer on an individual basis [10]. According to one study, logistic regression with a single age, previous
breast biopsies, and family history of breast cancer as inputs successfully predicted the probability of five-
year breast cancer [9].
Numerous earlier researches indicate how it is possible to improve the prediction power of the Gail
model by utilizing expensive inputs. Earlier research has established that when evaluated using the same data
set, models that incorporate one or more instructional inputs in addition to the Gail model predict better
breast cancer risks than the Gail model. These works make use of simple statistical models and incorporate
data gathered by costly and/or aggressive methods. Breast density measurements [11]–[13] genetic
single-nucleotide polymorphism measurements [12], [14]–[16] nipple aspirate fluid cytology measurements
[17], and/or hormone levels measurements [12], [18]. All of this was accomplished by employing simple
statistical models such as Cox proportional hazards regressions [11], [17], logistic regressions [12], [16], or
the Gail model. Due to these approaches’ limitations in terms of high-precision diagnosis, machine
learning-based methods have been created, which is the core emphasis of this work.
The study analyzes if modern machine learning algorithms can anticipate the risk of breast cancer
more accurately than BCRAT can in the next five years. Complex machine learning models can exploit
subtle patterns in the input data to improve forecasting accuracy. In [19] proved the efficacy of machine
learning algorithms in predicting endometrial cancer risk using personal health data. In [19], six machine
learning techniques were used to assess the probability of getting endometrial cancer: logistic regression,
naive Bayesian, decision tree (DT), linear discriminant analysis (LDA), support vector machine (SVM), and
artificial neural network (ANN). Internal validation was performed in the prostate, lung, colorectal, and
ovarian cancer screening trials (PLCO) by training models on 70% of the data and evaluating their
performance on the remaining 30% of the data [20]. A neural network was discovered to be the best predictor
of risk in [19]. The goal of this study is to determine whether breast cancer risk can be predicted using
machine learning algorithms, as well as endometrial cancer risk. By comparing the performance of
alternative machine learning models to the BCRAT function, one can determine whether a model with a
statistical foundation is more robust than BCRAT when given the same inputs and assessed on the same data
set [7]. In [21], The goal of this study is to determine Lifestyles such as smoking and physical activity were
suspected to affect breast cancer indirectly; however, comparing breast cancer patients between healthy and
unhealthy lifestyles needs to be proven future. Lifestyles such as smoking and physical activity were
suspected to affect breast cancer indirectly; however, comparing breast cancer patients between healthy and
unhealthy lifestyles needs to be proven future. Changes lifestyle such as quitting smoking, active exercise,
reducing alcohol consumption, taking vitamin and minerals seem an effective, easy, and economical ways to
help prevention of breast cancer. In [22], this study aimed to investigate the knowledge, attitude and practice
of women about breast cancer’s screening methods in order to offer more appropriate training programs if
necessary. A cross-sectional study was carried out with a population comprised of women who had referred
to public health centers in Sanandaj in 2008. The results of this study do provide some understanding on the
topic and suggest that although the majority of Iranian women seem to be quite knowledgeable about breast
cancer and screening methods.
The remainder of this article is: the sections on pre-processing, segmentation and feature extraction,
classification, and performance appraisal cover the stages involved in developing a computer-aided design
model using machine learning methods. Each section explains the designated stage in detail and the machine
learning approach used to perform it. The purpose of this article is to address the impact of applying machine
learning principles and algorithms to histology and the research gaps associated with their implementation in
a real-world setting. Finally, the conclusion makes recommendations for future research. The study’s
emphasis is on research employing mammography and histopathological pictures in all locations.
3. Int J Elec & Comp Eng ISSN: 2088-8708
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2. BREAST CANCER
A type of breast cancer that starts in women has symptoms such as a lump in the breast, a change in
the shape of the breast, a dimple in the skin, discharge from the nipple, or scaling of part of the skin. In order
to develop cancer, the gene for cell growth and proliferation must be altered. These mutations will then
become a mass through cell proliferation. By identifying the gene that transmits this cancer, an important step
can be taken in predicting breast cancer. One of the most important problems in showing the structure and
macro performance of biological molecules is the high volume of genetic information. Also, one of the most
important challenges in bioinformatics is that it requires the design and production of methods, algorithms,
and tools to transform this vast amount of data, often heterogeneous (at low levels), into higher-level
biological knowledge.
This article is based on breast cancer diagnosis and classification by considering mammography and
histopathology images mammograms are X-ray scans of the breast that can detect symptoms of breast cancer
in their early stages. A mammogram is produced using one of two ways. Digital mammography produces
digital images, whereas film-screen mammography produces photographic film. Both techniques employ the
same picture capture procedure. The woman undergoing the mammography will insert her breast between
two transparent plates, which will squeeze it together and secure it. This helps to flatten the breast and
prevents the image from becoming blurry. Two images of the breast are taken by the machine. The
mammogram is then analyzed by a professional for anything abnormal that could be a sign of cancer. Any
spot that does not appear to be normal tissue should be regarded as suspicious. The radiologist will search for
regions of white, dense tissue and take note of its size, shape, and borders. On a mammography, a lump or
tumor appears as a narrow white spot. Cancerous or benign tumors are also possible. A benign tumor does
not pose a risk to health and is unlikely to grow or change shape. The majority of breast tumors are benign.
Generally, small white flecks are benign. Their shape and pattern will be examined by the radiologist, as they
can occasionally be indicative of cancer. A radiologist will search for anything odd on a mammography, in
addition to thick breast tissue and suspected malignancies. Among the other anomalies: i) cysts, which are
tiny sacs filled with fluid. The majority are benign cysts with a thin wall that are not malignant. If a doctor is
unable to classify a cyst as a simple cyst, they may order additional testing to rule out cancer,
ii) calcifications are calcium deposits. Macrocalcifications are larger deposits of calcium that typically arise
as a result of aging. Microcalcifications are smaller deposits. Depending on the appearance of the
microcalcifications, a doctor may do a test to see if they are indicative of malignancy, iii) fibroadenomas are
benign breast tumors. They are spherical and may have the texture of a marble. Although fibroadenomas are
more common in people in their 20 s and 30 s, they can occur at any age, and iv) scar tissue is frequently
seen as white on mammograms. It is best to inform a doctor in advance of any scars on the breasts.
A mass may refer to a tumor, cyst, or fibroadenoma, regardless of whether they are cancerous.
Additionally, a mammography might provide information regarding a person’s breast density. Individuals
with thick breasts have a slightly increased risk of developing breast cancer. Dense breasts might make
problems more difficult to detect on a mammogram. Mammograms are still possible after breast cancer
surgery or implant placement. However, additional photos of each breast may be required, and image
verification may take longer. Frequently, a radiologist will compare a mammogram to earlier pictures. This
enables them to detect changes and determine whether an atypical area is indicative of malignancy.
Part of the X-ray radiation during the mammogram is absorbed in accordance with the tissue
conditions, and the other part passes. Tissue in proportion to its nature absorbs some of the energy. The rate
at which the signal leaves the cancerous tissue varies with the breast tissue. From the drop in the input to
output signal, it can be determined whether the tissue has a cancerous mass. Today’s mammography is based
on intelligent medical diagnostic systems, the main basis of which is image processing along with machine
learning principles. Principles of image processing in intelligent medical systems are important for
diagnosing breast cancer, because mammography images are inherently noisy, and this noise can be difficult
to diagnose by a doctor. Of course, intelligent medical diagnostic systems have the ability to eliminate noise
as well as diagnosis, but the doctor must give a definite opinion. Therefore, it is important to provide a smart
medical diagnosis system to diagnose breast cancer. The use of image processing principles and techniques,
along with statistical and cognitive identification of patterns in the diagnosis and automatic determination of
breast cancer from mammography images has reduced human error and increased detection speed. In this
study, we try to use the principles of image processing and machine learning to provide a system for
diagnosing breast cancer tumors and separating benign and malignant conditions.
3. PRE-PROCESSING
Pre-processing is essential to reduce the complexity and efficiency of the image. The pre-processing
stage minimizes image noise and aids in identifying focal points [23]. To boost local contrast, a limited
contrast-compatible histogram alignment is applied [24]. Thresholds are used to minimize image noise. As a
4. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 12, No. 6, December 2022: 6397-6409
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threshold value on the date of the picture intensity diagram, the following pixels are considered noise [23].
The Otsu threshold method is used to determine the ideal threshold [25]. To improve threshold results,
background correction and filtering are applied. Fixed field correction lessens the effect of changing lighting
conditions, while the filter reduces image noise [24]. The preceding strategies are widely used to enhance the
quality of digital pictures used in machine learning applications. Data amplification and color normalization
are the most often utilized pre-processing processes for computer-aided learning systems based on deep
learning. Scaling, flipping, mirroring, blurring, and adding noise to training data sets are all examples of data
augmentation. These changes correct the morphology of the image [26]. Additionally, histopathological
images vary in hue and brightness due to changes in histopathology slide preparation (stain staining) and
various noise circumstances when digital images are taken. As a result, stain normalization is a critical step in
the pre-processing of histopathological imaging [26]. A whole-colored histopathology picture is used as the
reference image in this procedure. The color values are adjusted to match the target image’s color values.
According to a study published in [27], stain normalization improves the performance of deep-learning
classifiers. Stain normalization was employed by researchers in [28], [29] to develop stain-based
computer-aided design tools for breast cancer histopathology. The study [30] presents a new evaluation of
color staining normalization approaches for histopathology pictures. Certain research investigations have
demonstrated extremely successful methods for normalizing color in digital photographs. For instance,
Reinhard et al. [31] suggested a method for converting an red green blue (RGB) image to a color space based
on perception (lab). By computing the mean and standard deviation for each axis of the image, the approach
transfers color between the source and target images. In [32], researchers suggested a quantitative analysis-
based strategy for adjusting the color of histopathology pictures. Khan et al. [33] proposed a staining method
in which the image stain matrix is calculated using a stain color descriptor. Following that, the image’s
varying stain concentration values are retrieved using color decomposition (stain separation) and passed to
the nonlinear mapping function (based on spline). Finally, the source image is recreated utilizing stained
channels in the normal manner. Stain normalization should be used in conjunction with color enhancement to
boost the performance of deep-learning computer-aided design systems, according to a recent study [26].
In [34], a review study and comparison of noise reduction methods in mammography images has
been performed and the strengths and weaknesses of these methods in the pre-processing phase have been
discussed. Also in [35], an analytical method of noise is presented in X-ray mammography images based on
non-local mean method. In [36], a comparative adaptive weighted frost filter has been used for
pre-processing to reduce noise in mammography images. In [37], the impact noise reduction in ultrasound
images was performed using the modified Bayes method. This study is based on a combination of intelligent
systems in [38]. The findings demonstrated the accuracy of breast cancer prediction and diagnosis. Although
adaptive fuzzy neural networks were used in this study for adaptation and learning, given the difficulty
associated with teaching this type of network, a combination of evolutionary algorithms and data mining
systems may be a novel idea for increasing the efficiency of prediction and estimation of adaptive fuzzy
neural networks with greater accuracy.
4. SEGMENTATION AND FEATURE EXTRACTION
After the pre-processing stage, they become vectors of a particular property [39]. Extraction plays a
vital role in the design of machine learning models [40]. Extraction techniques for features computer-aided
design can be divided into two categories: features that are handcrafted (manual design) and features that are
learned using deep learning. When constructing handcrafted features, the user must exert some effort to
determine whether characteristics are diagnostically acceptable. In contrast, deep learning models, while
teaching networking for classification, automatically determine key features. These two sections are
explained separately and an overview of its methods is given.
4.1. Handcrafted-based feature extraction methods
A computer-aided design system based on the division of graphical diagrams with space-color,
extraction of tissue properties (such as gray-level co-occurrence matrix (GLCM), grassland-based ruminant
livestock models (GRLM) and Euler methods) and classification based on linear discriminative analysis is
presented in [41]. The system categorized 70 histopathology and mammography images with 100% accuracy.
In [42], an accuracy of 97.75% was achieved using a feature-oriented vocabulary learning algorithm on a
data set of human intraocular lesions and animal diagnostic laboratories. A computer-aided design method
based on the extraction of morphological features was shown to be 85.7% accurate in classifying
histopathological and mammographic data sets from 70 images [43]. However, this research has a key
disadvantage in that it examined limited unpublished data sets for studies. Given this constraint, it is difficult
to compare these efforts to other research. To circumvent this constraint, the authors made publicly available
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Breast cancer diagnosis: a survey of pre-processing, segmentation, feature … (Ehsan Sadeghi Pour)
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a set of histopathology data and breast cancer mammography images dubbed BreakHis data and conducted
preliminary trials utilizing manual features. The authors discovered that parameter-free threshold adjacency
statistics (PFTAS) has the highest cancer detection rates in comparison to local binary pattern (LBP),
completed local binary patterns (CLBP), local phase quantization (LPQ), opening range breakout (ORB), and
GLCM. Another study used the Gabor filter, wavelet, and LBP features in conjunction with an ensemble
classifier to obtain 200x magnification and 90.32% accuracy for BreakHis picture classification. PFTAS
features, combined with a non-parametric classification method based on multi-sample learning, recently
achieved the greatest patient recognition rate (PRR) for the BreakHis data set [44]. However, the accuracy
and standard deviation of this research are quite high.
In [45], the use of cross-section and extraction of edge-based and area-based features in a four-step
system is ensured on a mammographic scale. Also in [46], wavelet transform and genetic algorithm are used
to segment and extract the characteristics of mammography images. The use of fuzzy logic for region of
interest is also provided for this purpose in [47]. In [48] used adaptive local thresholding methods and
improved morphology to extract and segment mammography and histochemistry images. In [49], a cellular
neural network was used along with region growing with the aim of segmentation and extraction of
mammographic image characteristics. The use of micro array images to diagnose breast cancer masses has
also been studied [50]. The use of back propagation neural networks to dissect and diagnose breast cancer
masses has also been discussed in [51]. The application of business theory classification method based on
Bayes theory in mammography images is also presented in [52]. In [53], a comparatively intelligent
decision-making system has been used to diagnose breast cancer based on mammographic images based on
evolutionary methods based on regression. In [54], the prediction of breast cancer recurrence is presented
using optimized ensemble learning or hybrid computer-aided-diagnosis system for prediction of breast cancer
recurrence (HPBCR).
4.2. Deep learning-based feature extraction methods
Deep learning-based feature extraction methods is highly regarded by researchers for classifying
histopathology and mammography images. In [55] represented that deep learning-based methods are more
accurate than autoimmune colors, textures, and geometric features for automatic detection of carcinoma
invasive tissues in images of total breast cancer slides. In [56], studies were conducted on histological data
from breast cancer using the LBP, CLBP, LPQ, ORB, GLCM, and PFTAS characteristics. Subsequently, the
same authors used a deep learning network to classify the BreakHis dataset in [57]. This model outperforms
classification models based on LBP, CLBP, LPQ, ORB, and GLCM features in terms of accuracy. Recently,
neural network convolution based on multivariate learning produced the maximum PRR for the BreakHis
data set [44].
The use of the convolutional neural network for segmentation and feature extraction is presented in
[58] along with a decoder and encryption mode. In [59], deep learning based on the conductive UNet 2
method has been used to segment breast tissue and fibroglandular. In [60], multi-task segmentation is
presented in several sections of mammography images to find deep learning breast masses and standard
convolutional neural network (CNN) methods. In [61], deep learning and the CNN method of V-net
convolution have been used to segment mammography images as well as prostate images. Mammography
imaging with the aim of diagnosing and classifying benign and malignant masses with an optimal area
growth approach is presented in [62], which is based on dragonfly optimization algorithm and a combined
approach of GLCM and GLRLM method for extracting features as input in the method and classification by
the feed-forward neural network or feed forward neural network (FFNN) has been used with back
propagation training. In [63], the proposed approach is used to crop the region of interests (ROIs) manually.
Based on that numbers of features are extracted. In this proposed method a novel hybrid optimum feature
selection (HOFS) method is used to find out the significant features to reach maximum accuracy for this
classification. A number of selected features are applied to train the neural network. In this proposed method
accessible informational index from the mini-mammographic image analysis society (MIAS) database was
used. In [64], the purpose of this article was to review various approaches to detecting breast cancer using
artificial intelligence (AI) and image processing. The authors present an innovative approach for identifying
breast cancer using machine learning methods. Compared to current approaches, such as CNN, our particle
swarm optimized wavelet neural network (PSOWNN) method appears to be relatively superior.
5. CLASSIFICATION
Classification is the process of categorizing data based on inherent quantitative information [65]. To
diagnose breast cancer, data points should be classed as benign or malignant. Two stages comprise the
categorization model: training and testing. The classifier is trained by providing it with features vectors
containing class labels as input. These feature vectors can be thought of as learning instances for the
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classifier. After teaching samples to classify, they can be utilized to evaluate a feature vector corresponding
to an unknown class. The output of the test is a class tag attached to a feature vector. The DT, the naive
Bayesian technique, the K-nearest neighbor (KNN) approach, the ANN, SVM, and ensemble classifier are all
examples of machine learning approaches that have been widely used to develop computer-aided design
learning models [66], [67]. In [68] explored the applications, advantages, and limitations of these classifiers
for cancer prediction and detection. This section discusses classification approaches that utilize deep learning
systems and neural networks.
The use of ensemble classifiers has been fully studied in [68]. In this study, 193 articles published
from 2000 to 2019 were compared with 9 different evaluation criteria in terms of efficiency. The use of
histopathological and mammographic images with the aim of classifying cancer masses in the breast area has
been reviewed in [69] based on machine learning methods. In [70], [71], the XCSR and XCSLA
classification system for diagnosing some of the diseases are that it can also be used to diagnose breast
cancer. In [72] another study, the use of an extreme learning machine with deep convolution features was
presented with the aim of diagnosing and classifying breast cancer. In fact, the use of extreme learning
machines is aimed at improving accuracy in the classification.
In [73], deep learning training for the classification of histopathological and mammography images
for the cancer masses detection in breast area has been proposed. The cold metal transfer (CMT) data set was
intended to be used in this study. Also, the deep neural network type was VGGNet-16, which achieved a high
accuracy of about 93% to 97%. In [74], a combination of a support vector machine method with a deep
neural network with the aim of classifying mammographic images to detect cancer masses has been used.
The accuracy of this method was about 94% to 98% depending on the different data sets. In [75], [76], CNN
has been used to diagnose and classify masses in the breast area, the results demonstrated by classical
methods such as SVM, naïve Bayesian and other neural networks.
In [77], an end-to-end training approach with deep learning proposed for breast cancer classification
by using curated breast imaging subset-digital database for screening mammography (CBIS-DDSM). In [78],
one of the best review articles has been compiled which notice to the main points and challenges of deep
learning for breast cancer classification in imaging data. Advances in deep learning for cancer diagnosis,
particularly breast cancer, were discussed in [79]. Deep learning algorithms displayed expert-level
performance in detecting breast cancer metastases in lymph nodes, outperforming prior feature-engineered
methods of histopathology analysis. Additionally, this study on deep learning enables large-scale
morphology-based research, as demonstrated recently in the mapping and analysis of tumor infiltrating
lymphocyte patterns in hundreds of specimens from the Cancer Genome Atlas digital slide archive. Another
systematic review [80] summarized the current state of the art for computer-aided diagnosis methods for
breast cancer. Based on this research collecting data and processing to find an optimal solution of breast
cancer diagnosis and classification proposed based on machine learning and as the main results, deep
learning methods have the most advantages for this job. Another semantic segmentation and classification of
breast cancer masses proposed in [81] which used deep learning. In this method, human epidermal growth
factor receptor-2 deep neural network (Her2Net) and trapezoidal long short-term memory (TLSTM) used as
deep learning algorithm to segment and classify cell membranes and nuclei in breast area for evaluation. This
method had high accuracy about 98.33%. In this study [82], both of linear discriminant analysis and SVM are
compared by looking from accuracy, sensitivity, specificity, and F1-score. We will know which methods are
better in classifying breast cancer dataset. The result shows that the support vector machine has better
performance than the linear discriminant analysis. It can be seen from the accuracy is 98.77%.
6. METHODS AND RESULTS
To this day, the following methods of breast tumor diagnosis in frequency images have been
presented: i) morphological methods, ii) based on machine learning, iii) based on fuzzy logic, iv) based on
evolutionary algorithms, and v) methods implementing chaos theory. These algorithms also include hybrid
models based on the following single algorithms: i) C-means, ii) K-means, iii) fuzzy C-means (FCM),
iv) fisher, and v) H-means. The hybrid diagnosis models are based on the following algorithms: i) genetic
algorithm and FCM, ii) diagnosis based on multilayer perceptron neural network and optimized particle
swarm algorithm. The following neural network-based classification methods are recommended: i) multilayer
perceptron neural network, ii) adaptive neural oscillator network, iii) Hopfield neural network,
iv) probabilistic neural network, v) radial basis function neural network, vi) self-organizing map neural
network, vii) neocognitron neural network, and viii) Grossberg neural network. The other recommended
evolutionary algorithms to improve segmentation and create optimal classes for breast cancer diagnosis from
images include: i) gray wolf optimization algorithm, ii) dragonfly algorithm, iii) social spider algorithm,
iv) bacterial foraging algorithm, v) ant colony algorithm, and vi) multi-objective genetic algorithm.
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Adaptive filters such as least mean square (LMS), recursive least square (RLS) or normalized least
mean square (NLMS) used in signal processing is a novel approach that combines image and signal
processing to compress images into matrices with rows and columns of features. Then, a dimensionality
reduction method should select and extract features to remove redundant features. The fractal theory is
another similar compression method, and the Bézier or Brownian curves can also be used for this purpose.
Chaos theory can use Chebyshev, Lyapunov, Lorenz, and other equations for stable and resistant
segmentation, and despite their computational complexity, can greatly improve accuracy and sensitivity.
Table 1 (see in appendix) summarizes the important breast tumor diagnosis and classification studies with the
strengths and weaknesses of proposed methods. To compare the various methods presented so far in terms of
evaluation criteria, most studies use accuracy percentage as the main breast cancer diagnosis and
classification comparison criterion. Table 2 compares studies using the same dataset, e.g. the MIAS dataset.
A comparison can be made here between classical methods, deep learning algorithms and extreme learning
machines which are listed in Table 3.
In general, it can be argued that edge localized mode (ELM) is exactly the opposite of deep learning
methods and alternative classification methods such as SVM and Naïve Bayesian. The ELM algorithm can
employ nonlinear activation functions such as sigmoid or sinusoidal or non-derivative activation functions as
well as using a linear function to activate cells or neurons in the hidden layer because of its high flexibility.
Table 2. Compares studies using the same dataset
Reference Accuracy )%(
Rouhi et al. 2015 [49] 96.47 %
Khalilabad and Hassanpour 2017 [50] 95.45 %
Kaymak et al. 2017 [51] 70.40 %
Karabatak 2015 [52] 98.54 %
Wang et al. 2018 [53] 97.10 %
Geweid and Abdallah 2019 [83] 85 %
Table 3. A case study for breast cancer diagnosis and classification
Naïve Bayesian SVM CNN Recursive neural network
(RNN)
ELM
Slow training
mechanism
Slow training
mechanism
Slow training mechanism Slow training mechanism Slow training
mechanism
binary
classification
ability
binary classification
ability
Multi-class classification
ability
Multi-class classification
ability
Multi-class classification
ability and also multi-
objective and real-time
Quadratic
Programming
Quadratic programming Non-linear multi-objective
programming
Multi-objective Quadratic
programming
Non-linear multi-class
and multi-objective
programming
Improved
evaluation criteria
such as accuracy,
sensitivity, and
feature rate
Improved diagnosis and
classification of benign
and malignant tumors
and determination of
exact tumor area
Improved accuracy,
features, and sensitivity
relative to the New
Bayesian method, random
forest algorithm, support
vector machine, and K
nearest neighbor
Accurate tumor diagnosis
and identification
Accurate tumor
diagnosis and
identification
High
computational
complexity and
run time
Failure to discriminate and
classify benign and
malignant tumors and
compare the proposed
approach with previous
deep learning methods
Very computationally and
time intensive
Slow tumor diagnosis
Very computationally
intensive and inaccurate
comparison without
mentioning used data
(methods should compare the
data in a similar body state.)
Very
computationally-
intensive
7. CONCLUSION
According to studies in the field of breast cancer diagnosis with machine learning methods, it was
observed that there are several basic steps in it, which include pre-processing, segmentation and features
extraction, and finally classification. A variety of methods have been studied in these three sections from 93
different references over the years. Based on the available analysis and results of these articles, it can be seen
deep learning methods have high capabilities in the field of pre-processing, segmentation and features
extraction, and also in classification. However, a variety of studies have been performed with these methods,
including the CNN and other similar methods combined with other methods. Therefore, it will be interesting
to present an intelligent and automatic method that can detect and classify benign and malignant cancer
masses from mammography, histopathology and any other type of visual data in the field of breast cancer
diagnosis. Also, considering evaluation criteria such as accuracy, sensitivity, specificity, mean square error,
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recall, receiver operating characteristic (ROC), area under curve (AUC) rate and other evaluation criteria is
necessary to compare with previous methods especially with deep learning methods. Therefore, in future
research, a combined method will be used with the opposite point of deep learning algorithms, namely,
extreme learning machine with combination to some optimal methods.
8. FUTURE WORKS
This study was the point of view of recent methods for pre-processing, segmentation with feature
extraction and also classification of breast cancer. Based on this study, it is observed that deep learning
methods obtained the best results in any parts of breast cancer classification and diagnosis and also in pre-
processing, segmentation and feature extraction. Creating classes for determining benign and malignant
masses in breast are I one of the open challenges. Another challenge is to find the exact area of tumor and
also estimating its size. Also increasing some evaluation criteria such as accuracy, sensitivity, specificity,
ROC, AUC, runtime, and others, is the main parts of these challenges in any parts. So, as the future works, it
will be study about different parts of breast cancer diagnosis and classification: i) pre-processing by
proposing optimized and enhanced method for reducing mammogram noises; ii) proposing evolutionary
algorithm for segmentation and feature extraction to find the best features of images. This is a vital part of
this algorithm due to finding exact area of masses in images which will be then classify to find the types of
masses; and iii) classification which divided images and founded masses in three parts such as benign,
malignant and suspicious. These three creativity and contribution will be explained in details in other articles.
APPENDIX
Table 1. Summary of reviewed methods (continue)
Reference Method Advantages Disadvantages
Rouhi et al. 2015
[49]
Cellular neural network regional
growth segmentation with a specific
threshold and improved classification
parameters based on a genetic
algorithm in mammographic images
Improved accuracy, features, and
sensitivity relative to the new
Bayesian method, random forest
algorithm, support vector machine,
and K nearest neighbor
Very computationally and time
intensive
Slow tumor diagnosis
Khalilabad and
Hassanpour 2016
[50]
Breast tumor diagnosis using
microarray mammographic
images
More accurate tumor diagnosis Incorrect diagnosis of the
tumor areas
Noisy images after processing
Kaymak et al.
2017 [51]
Mammographic image
classification for breast cancer
diagnosis using the
backpropagation neural network
Determination of tumor area Poor tumor detection accuracy
from datasets and slow and
computationally-intensive
processing
Karabatak 2015
[52]
Tumor classification and diagnosis
using the new Bayesian method
Improved evaluation criteria such as
accuracy, sensitivity, and feature rate
High computational
complexity and run time
Wang et al. 2018
[53]
Adaptive intelligent decision-
making system for breast cancer
diagnosis from mammographic
images using regression analysis
Ability to diagnose tumors and
estimate life expectancy with or
without tumors and their exact
location in images
Poor tumor location accuracy
with high computational
complexity
Kaur et al. 2019
[74]
Segmentation-based breast tumors
diagnosis and classification from
mammographic images using the
K-means and SURF algorithms and
hybrid multiclass support vector
machine-deep learning classification
Improved diagnosis and classification
of benign and malignant tumors and
determination of exact tumor area
Failure to discriminate and
classify benign and malignant
tumors and compare the
proposed approach with
previous deep learning
methods
Yassin et al.
2018 [80]
Review Study Analysis of intelligent breast cancer
diagnosis methods and more efficient
neural networks
Failure to evaluate denoising
and segmentation methods
before feature extraction and
final classification
Geweid and
Abdallah 2019
[83]
Breast cancer diagnosis using
M-level optimization functions
based on a non-parametric pixel
intensity method
Detailed analysis of mammography
images and malignant tumors with
partial differential equation, non-
coplanar motion in breast cancer and
its overall dynamics, five simple
differential equations, and estimation
of intensity using nonparametric
techniques are useful as comparison
criteria.
High computational complexity
and failure to consider
suspicious and benign tumors
and specify data
Inaccurate comparison without
stating used data (methods
should compare the data in a
fixed body state.)
Guo et al. 2017
[84]
Review study Analysis of intelligent breast cancer
diagnosis methods and more efficient
neural networks
Failure to evaluate denoising
and segmentation methods
before feature extraction and
final classification
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Table 1. Summary of reviewed methods
Reference Method Advantages Disadvantages
Patel and Sinha
2014 [85]
Breast cancer diagnosis from
mammographic images using feature
analysis, preprocessing, and an
optimal classifier
Better evaluation criteria
including accuracy,
sensitivity, and feature rate
and diagnosis of benign
and malignant tumors
High computational complexity and run
time
Singh and
Gupta 2015
[86]
Breast cancer diagnosis from
mammographic images with
preprocessing followed by smoothing
and thresholding for feature extraction
Continuous window-finding with
lower-variance min-max to determine
tumor areas and sizes in images based
on morphological operations and
image gradient technique.
Improved evaluations
criteria including accuracy,
sensitivity, and feature rate
with correct diagnosis of
the tumor area
High computational complexity and run
time
Tang et al.
2009 [87]
Review Study Analysis of intelligent
breast cancer diagnosis
methods and more efficient
neural networks
Failure to evaluate denoising and
segmentation methods before feature
extraction and final classification
Lee 2019 [88] Performance analysis of the Compton
camera with the Si/CZT lens for
breast tumor diagnosis using the
Monte Carlo method
Accurate determinations of
tumor area in two and
three-dimensional images
Inability to segment and classify benign
and malignant tumors and analyze
results and evaluation criteria
Khan et al.
2019 [89]
Implementing the GoogLeNet,
VGGNet, and ResNet deep learning
architectures to diagnose and classify
breast cancer from mammographic
images
Improved diagnosis and
classification of benign and
malignant tumors and
determination of exact
tumor area
Failure to mention the deep learning
structure created in hidden layers,
including fully connected layers,
pooling layers, and the convolve layer,
High computational complexity and run
time in diagnosis and classification
Rahmatinia and
Fahimi 2017
[90]
Breast tumor diagnosis with
thermographic methods and high-
frequency stimulation based on
radiofrequency
Stimulation and accurate
determination of tumor
area
Computationally-intensive without
discrimination of tumor states after
diagnosis
Wang et al.
2019 [91]
Breast cancer diagnosis based on
fusion features, convolution neural
network, and extreme learning
machine (ELM) classification
Accurate tumor diagnosis
and identification
Very computationally-intensive
Li et al. 2019
[92]
Extracting a distinct pattern for breast
cancer histopathological image
classification using the automatic
structure based on convulsion neural
network and support vector machine
Accurate tumor diagnosis
and identification
Very computationally intensive and
inaccurate comparison without
mentioning used data (methods
should compare the data in a similar
body state).
Panesar et al.
2017 [93]
Breast cancer diagnosis using a
biosensor structure with quantum dots
for tracking breast cancer mRNAs
Applying biosensor
principles to diagnose
breast tumors and present a
novel optical and quantum
processing method
Failure to review denoising and
segmentation methods before feature
extraction and final classification -
Failure to present final results and
evaluation and comparison
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BIOGRAPHIES OF AUTHORS
Ehsan Sadeghi Pour is currently pursuing the Ph.D. degree in Computer
Engineering-Artificial Intelligence and Robotics, Islamic Azad University, Kashan branch,
Iran. He received Bs in software engineering from the Birjand University of Industries and
Mines, Birjand and his M.Sc. in Computer Engineering, Artificial Intelligence from University
of Hormozgan, Bandar Abbas in Iran. He is a faculty member at University of Sama in Bandar
Abbas, Iran. His research interests include artificial intelligence, machine/deep learning, data
mining, computer vision and diagnosis. He can be contacted at email: Ehsan61.ai@gmail.com.
13. Int J Elec & Comp Eng ISSN: 2088-8708
Breast cancer diagnosis: a survey of pre-processing, segmentation, feature … (Ehsan Sadeghi Pour)
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Mahdi Esmaeili received his Ph.D. degree in Computer Engineering at
University of Debrecen in Hungary. He received a Bs in software engineering from the
University of Isfahan, and his MS in computer science at 1999 in Iran. He is a faculty member
of Islamic Azad University (Kashan branch). He has taught in the areas of database and data
mining and his research interests include data base, data mining and knowledge discovery, and
recent research focusing on the end use of data mining. He has published books about database
and information retrieval and also has published articles in international conferences. He can
be contacted at email: M.esmaeili@iaukashan.ac.ir.
Morteza Romoozi received his Ph.D. degree in Computer Engineering from
Science and research branch of Islamic Azad University in Iran. He received Bs in software
engineering from the University of Kashan at 2003, and his MS in computer science at 2006 in
Iran. He is a faculty member of Islamic Azad University (Kashan branch). He has taught in the
areas of wireless networks, ad hoc and P2P Networks and his research interests include
semantic web, information retrieval, and recent research focusing on the mobility model and
routing protocol in ad-hoc networks. He has published several articles in international
conferences, LNCS series and other springer journals. He can be contacted at email:
Mromoozi@gmail.com.