Breast cancer has become a common factor now-a-days. Despite the fact, not all general hospitals
have the facilities to diagnose breast cancer through mammograms. Waiting for diagnosing a breast
cancer for a long time may increase the possibility of the cancer spreading. Therefore a computerized
breast cancer diagnosis has been developed to reduce the time taken to diagnose the breast cancer and
reduce the death rate. This paper summarizes the survey on breast cancer diagnosis using various machine
learning algorithms and methods, which are used to improve the accuracy of predicting cancer. This survey
can also help us to know about number of papers that are implemented to diagnose the breast cancer.
Predictive Analysis of Breast Cancer Detection using Classification AlgorithmSushanti Acharya
Dissertation project titled “Predictive analysis of Breast Cancer detection using Classification”. For the research conducted, Breast Cancer Wisconsin Diagnostics dataset was used for analysis. Using R language machine learning model was designed based on various algorithms and the derived results were then visualized to present the most accurate model of them all (SVM in this case).
Breast Cancer Diagnostics with Bayesian NetworksBayesia USA
The Wisconsin Breast Cancer Database (WBCD) is a widely studied (and publicly available) data set from the field of breast cancer diagnostics. The creators of this database, Wolberg, Street, Heisey and Managasarian, made an important contribution with their research towards automating diagnostics with image processing and machine learning.
Beyond the medical field, many statisticians and computer scientists have proposed a wide range of classification models based on WBCD. Such new methods have continuously raised the benchmark in terms of diagnostic performance.
Our white paper now reevaluates the Wisconsin Breast Cancer Database within the framework of Bayesian networks, which, to our knowledge, has not been done before. We demonstrate how the BayesiaLab software can extremely quickly — and simply — create a Bayesian network model that is on par performance-wise with virtually all existing models that have been developed from WBCD over the last 15 years.
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.
Detecting malaria using a deep convolutional neural networkYusuf Brima
Experiment with Deep Residual Convolutional Neural Network to classify microscopic blood cell images (Uninfected, Parasitized)
Utiling ResNet,Deep Residual Learning for Image Recognition (He et al, 2015) architecture.
Uses Keras with a Tensorflow backend.
Breast Cancer Detection from Mammography Images Using Machine Learning Algorithms (U-Net Segmentation and Dense Net Classifier implementation are in progress)
Predictive Analysis of Breast Cancer Detection using Classification AlgorithmSushanti Acharya
Dissertation project titled “Predictive analysis of Breast Cancer detection using Classification”. For the research conducted, Breast Cancer Wisconsin Diagnostics dataset was used for analysis. Using R language machine learning model was designed based on various algorithms and the derived results were then visualized to present the most accurate model of them all (SVM in this case).
Breast Cancer Diagnostics with Bayesian NetworksBayesia USA
The Wisconsin Breast Cancer Database (WBCD) is a widely studied (and publicly available) data set from the field of breast cancer diagnostics. The creators of this database, Wolberg, Street, Heisey and Managasarian, made an important contribution with their research towards automating diagnostics with image processing and machine learning.
Beyond the medical field, many statisticians and computer scientists have proposed a wide range of classification models based on WBCD. Such new methods have continuously raised the benchmark in terms of diagnostic performance.
Our white paper now reevaluates the Wisconsin Breast Cancer Database within the framework of Bayesian networks, which, to our knowledge, has not been done before. We demonstrate how the BayesiaLab software can extremely quickly — and simply — create a Bayesian network model that is on par performance-wise with virtually all existing models that have been developed from WBCD over the last 15 years.
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.
Detecting malaria using a deep convolutional neural networkYusuf Brima
Experiment with Deep Residual Convolutional Neural Network to classify microscopic blood cell images (Uninfected, Parasitized)
Utiling ResNet,Deep Residual Learning for Image Recognition (He et al, 2015) architecture.
Uses Keras with a Tensorflow backend.
Breast Cancer Detection from Mammography Images Using Machine Learning Algorithms (U-Net Segmentation and Dense Net Classifier implementation are in progress)
Machine Learning - Breast Cancer DiagnosisPramod Sharma
Machine learning is helping in making smart decisions faster. In this presentation measurements carried out on FNAC was analysed. The results were validated using 20 percent of the data. The data used for POC is from UCI Repository/
Early detection of breast cancer using mammography images and software engine...TELKOMNIKA JOURNAL
The breast cancer has affected a wide region of women as a particular case. Therefore, different researchers have focused on the early detection of this disease to overcome it in efficient way. In this paper, an early breast cancer detection system has been proposed based on mammography images. The proposed system adopts deep-learning technique to increase the accuracy of detection. The convolutional neural network (CNN) model is considered for preparing the datasets of training and test. It is important to note that the software engineering process model has been adopted in constructing the proposed algorithm. This is to increase the reliably, flexibility and extendibility of the system. The user interfaces of the system are designed as a website used at country side general purpose (GP) health centers for early detection to the disease under lacking in specialist medical staff. The obtained results show the efficiency of the proposed system in terms of accuracy up to more than 90% and decrease the efforts of medical staff as well as helping the patients. As a conclusion, the proposed system can help patients by early detecting the breast cancer at far places from hospital and referring them to nearest specialist center.
Breast cancer diagnosis and recurrence prediction using machine learning tech...eSAT Journals
Abstract Breast Cancer has become the common cause of death among women. Due to long hours invested in manual diagnosis and lesser diagnostic system available emphasize the development of automated diagnosis for early diagnosis of the disease. Our aim is to classify whether the breast cancer is benign or malignant and predict the recurrence and non-recurrence of malignant cases after a certain period. To achieve this we have used machine learning techniques such as Support Vector Machine, Logistic Regression, KNN and Naive Bayes. These techniques are coded in MATLAB using UCI machine learning depository. We have compared the accuracies of different techniques and observed the results. We found SVM most suited for predictive analysis and KNN performed best for our overall methodology. Keywords: Breast Cancer, SVM, KNN, Naive Bayes, Logistic Regression, Classification.
For the scope of this project, it was decided to analyze the data to form distinct clusters based on their tumor type. Unsupervised learning (K-means clustering and hierarchical clustering) were used. Also, it was decided to analyze this data as a classification task. Based on different attributes (primarily mass spectrometry analysis results for 12553 proteins) few classification algorithms were implemented to see if the model can generate the accurate label of cancer type.
An Analysis of The Methods Employed for Breast Cancer Diagnosis IJORCS
Breast cancer research over the last decade has been tremendous. The ground breaking innovations and novel methods help in the early detection, in setting the stages of the therapy and in assessing the response of the patient to the treatment. The prediction of the recurrent cancer is also crucial for the survival of the patient. This paper studies various techniques used for the diagnosis of breast cancer. Different methods are explored for their merits and de-merits for the diagnosis of breast lesion. Some of the methods are yet unproven but the studies look very encouraging. It was found that the recent use of the combination of Artificial Neural Networks in most of the instances gives accurate results for the diagnosis of breast cancer and their use can also be extended to other diseases.
Breast cancer diagnosis via data mining performance analysis of seven differe...cseij
According to World Health Organization (WHO), breast cancer is the top cancer in women both in the
developed and the developing world. Increased life expectancy, urbanization and adoption of western
lifestyles trigger the occurrence of breast cancer in the developing world. Most cancer events are
diagnosed in the late phases of the illness and so, early detection in order to improve breast cancer
outcome and survival is very crucial.
In this study, it is intended to contribute to the early diagnosis of breast cancer. An analysis on breast
cancer diagnoses for the patients is given. For the purpose, first of all, data about the patients whose
cancers’ have already been diagnosed is gathered and they are arranged, and then whether the other
patients are in trouble with breast cancer is tried to be predicted under cover of those data. Predictions of
the other patients are realized through seven different algorithms and the accuracies of those have been
given. The data about the patients have been taken from UCI Machine Learning Repository thanks to Dr.
William H. Wolberg from the University of Wisconsin Hospitals, Madison. During the prediction process,
RapidMiner 5.0 data mining tool is used to apply data mining with the desired algorithms.
Possibilistic Fuzzy C Means Algorithm For Mass classificaion In Digital Mammo...IJERA Editor
Mammography is an effective imaging modality of breast cancer abnormalities detection. Survival rate of breast cancer treatment can be increased via early detection of mammography. However detecting the mass in the early stage is a tough task for radiologist. Detection of suspicious abnormalities is a continual task. Out of thousand cases only 3 to 4 are analyzed as cancerous by a radiologist and thus abnormality may be left out. 10-30% of cancers are failed to detect by radiologist. Computer Aided Diagnosis helps the radiologists to detect abnormalities earlier than traditional procedures. Because of some negligence in capturing device, the image may be affected by noise this leads to fault diagnosis. Preprocessing can remove this unwanted noise. In this paper features such as entropy, circularity, edge detection, and correlation are extracted from the image to distinguish normal and abnormal regions of a mammogram. Classification and detection of mammogram can be done by Possibilistic Fuzzy C Means algorithm and Support Vector Machine using extracted features.
Twin support vector machine using kernel function for colorectal cancer detec...journalBEEI
Nowadays, machine learning technology is needed in the medical field. therefore, this research is useful for solving problems in the medical field by using machine learning. Many cases of colorectal cancer are diagnosed late. When colorectal cancer is detected, the cancer is usually well developed. Machine learning is an approach that is part of artificial intelligence and can detect colorectal cancer early. This study discusses colorectal cancer detection using twin support vector machine (SVM) method and kernel function i.e. linear kernels, polynomial kernels, RBF kernels, and gaussian kernels. By comparing the accuracy and running time, then we will know which method is better in classifying the colorectal cancer dataset that we get from Al-Islam Hospital, Bandung, Indonesia. The results showed that polynomial kernels has better accuracy and running time. It can be seen with a maximum accuracy of twin SVM using polynomial kernels 86% and 0.502 seconds running time.
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.
Breast cancer is the leading cause of death for women worldwide. Cancer can be discovered early, lowering the rate of death. Machine learning techniques are a hot field of research, and they have been shown to be helpful in cancer prediction and early detection. The primary purpose of this research is to identify which machine learning algorithms are the most successful in predicting and diagnosing breast cancer, according to five criteria: specificity, sensitivity, precision, accuracy, and F1 score. The project is finished in the Anaconda environment, which uses Python's NumPy and SciPy numerical and scientific libraries as well as matplotlib and Pandas. In this study, the Wisconsin diagnostic breast cancer dataset was used to evaluate eleven machine learning classifiers: decision tree, quadratic discriminant analysis, AdaBoost, Bagging meta estimator, Extra randomized trees, Gaussian process classifier, Ridge, Gaussian nave Bayes, k-Nearest neighbors, multilayer perceptron, and support vector classifier. During performance analysis, extremely randomized trees outperformed all other classifiers with an F1-score of 96.77% after data collection and data analysis.
The Evolution and Impact of Medical Science Journals in Advancing Healthcaresana473753
Medical science journals have evolved into essential tools for advancing healthcare by disseminating research findings, promoting evidence-based practices, and fostering collaboration. Their historical significance, role in evidence-based medicine, and adaptability to the digital age make them indispensable in the quest for improved healthcare outcomes. As they continue to evolve, medical science journals will play a vital role in shaping the future of medicine and healthcare worldwide.
Machine Learning - Breast Cancer DiagnosisPramod Sharma
Machine learning is helping in making smart decisions faster. In this presentation measurements carried out on FNAC was analysed. The results were validated using 20 percent of the data. The data used for POC is from UCI Repository/
Early detection of breast cancer using mammography images and software engine...TELKOMNIKA JOURNAL
The breast cancer has affected a wide region of women as a particular case. Therefore, different researchers have focused on the early detection of this disease to overcome it in efficient way. In this paper, an early breast cancer detection system has been proposed based on mammography images. The proposed system adopts deep-learning technique to increase the accuracy of detection. The convolutional neural network (CNN) model is considered for preparing the datasets of training and test. It is important to note that the software engineering process model has been adopted in constructing the proposed algorithm. This is to increase the reliably, flexibility and extendibility of the system. The user interfaces of the system are designed as a website used at country side general purpose (GP) health centers for early detection to the disease under lacking in specialist medical staff. The obtained results show the efficiency of the proposed system in terms of accuracy up to more than 90% and decrease the efforts of medical staff as well as helping the patients. As a conclusion, the proposed system can help patients by early detecting the breast cancer at far places from hospital and referring them to nearest specialist center.
Breast cancer diagnosis and recurrence prediction using machine learning tech...eSAT Journals
Abstract Breast Cancer has become the common cause of death among women. Due to long hours invested in manual diagnosis and lesser diagnostic system available emphasize the development of automated diagnosis for early diagnosis of the disease. Our aim is to classify whether the breast cancer is benign or malignant and predict the recurrence and non-recurrence of malignant cases after a certain period. To achieve this we have used machine learning techniques such as Support Vector Machine, Logistic Regression, KNN and Naive Bayes. These techniques are coded in MATLAB using UCI machine learning depository. We have compared the accuracies of different techniques and observed the results. We found SVM most suited for predictive analysis and KNN performed best for our overall methodology. Keywords: Breast Cancer, SVM, KNN, Naive Bayes, Logistic Regression, Classification.
For the scope of this project, it was decided to analyze the data to form distinct clusters based on their tumor type. Unsupervised learning (K-means clustering and hierarchical clustering) were used. Also, it was decided to analyze this data as a classification task. Based on different attributes (primarily mass spectrometry analysis results for 12553 proteins) few classification algorithms were implemented to see if the model can generate the accurate label of cancer type.
An Analysis of The Methods Employed for Breast Cancer Diagnosis IJORCS
Breast cancer research over the last decade has been tremendous. The ground breaking innovations and novel methods help in the early detection, in setting the stages of the therapy and in assessing the response of the patient to the treatment. The prediction of the recurrent cancer is also crucial for the survival of the patient. This paper studies various techniques used for the diagnosis of breast cancer. Different methods are explored for their merits and de-merits for the diagnosis of breast lesion. Some of the methods are yet unproven but the studies look very encouraging. It was found that the recent use of the combination of Artificial Neural Networks in most of the instances gives accurate results for the diagnosis of breast cancer and their use can also be extended to other diseases.
Breast cancer diagnosis via data mining performance analysis of seven differe...cseij
According to World Health Organization (WHO), breast cancer is the top cancer in women both in the
developed and the developing world. Increased life expectancy, urbanization and adoption of western
lifestyles trigger the occurrence of breast cancer in the developing world. Most cancer events are
diagnosed in the late phases of the illness and so, early detection in order to improve breast cancer
outcome and survival is very crucial.
In this study, it is intended to contribute to the early diagnosis of breast cancer. An analysis on breast
cancer diagnoses for the patients is given. For the purpose, first of all, data about the patients whose
cancers’ have already been diagnosed is gathered and they are arranged, and then whether the other
patients are in trouble with breast cancer is tried to be predicted under cover of those data. Predictions of
the other patients are realized through seven different algorithms and the accuracies of those have been
given. The data about the patients have been taken from UCI Machine Learning Repository thanks to Dr.
William H. Wolberg from the University of Wisconsin Hospitals, Madison. During the prediction process,
RapidMiner 5.0 data mining tool is used to apply data mining with the desired algorithms.
Possibilistic Fuzzy C Means Algorithm For Mass classificaion In Digital Mammo...IJERA Editor
Mammography is an effective imaging modality of breast cancer abnormalities detection. Survival rate of breast cancer treatment can be increased via early detection of mammography. However detecting the mass in the early stage is a tough task for radiologist. Detection of suspicious abnormalities is a continual task. Out of thousand cases only 3 to 4 are analyzed as cancerous by a radiologist and thus abnormality may be left out. 10-30% of cancers are failed to detect by radiologist. Computer Aided Diagnosis helps the radiologists to detect abnormalities earlier than traditional procedures. Because of some negligence in capturing device, the image may be affected by noise this leads to fault diagnosis. Preprocessing can remove this unwanted noise. In this paper features such as entropy, circularity, edge detection, and correlation are extracted from the image to distinguish normal and abnormal regions of a mammogram. Classification and detection of mammogram can be done by Possibilistic Fuzzy C Means algorithm and Support Vector Machine using extracted features.
Twin support vector machine using kernel function for colorectal cancer detec...journalBEEI
Nowadays, machine learning technology is needed in the medical field. therefore, this research is useful for solving problems in the medical field by using machine learning. Many cases of colorectal cancer are diagnosed late. When colorectal cancer is detected, the cancer is usually well developed. Machine learning is an approach that is part of artificial intelligence and can detect colorectal cancer early. This study discusses colorectal cancer detection using twin support vector machine (SVM) method and kernel function i.e. linear kernels, polynomial kernels, RBF kernels, and gaussian kernels. By comparing the accuracy and running time, then we will know which method is better in classifying the colorectal cancer dataset that we get from Al-Islam Hospital, Bandung, Indonesia. The results showed that polynomial kernels has better accuracy and running time. It can be seen with a maximum accuracy of twin SVM using polynomial kernels 86% and 0.502 seconds running time.
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.
Breast cancer is the leading cause of death for women worldwide. Cancer can be discovered early, lowering the rate of death. Machine learning techniques are a hot field of research, and they have been shown to be helpful in cancer prediction and early detection. The primary purpose of this research is to identify which machine learning algorithms are the most successful in predicting and diagnosing breast cancer, according to five criteria: specificity, sensitivity, precision, accuracy, and F1 score. The project is finished in the Anaconda environment, which uses Python's NumPy and SciPy numerical and scientific libraries as well as matplotlib and Pandas. In this study, the Wisconsin diagnostic breast cancer dataset was used to evaluate eleven machine learning classifiers: decision tree, quadratic discriminant analysis, AdaBoost, Bagging meta estimator, Extra randomized trees, Gaussian process classifier, Ridge, Gaussian nave Bayes, k-Nearest neighbors, multilayer perceptron, and support vector classifier. During performance analysis, extremely randomized trees outperformed all other classifiers with an F1-score of 96.77% after data collection and data analysis.
The Evolution and Impact of Medical Science Journals in Advancing Healthcaresana473753
Medical science journals have evolved into essential tools for advancing healthcare by disseminating research findings, promoting evidence-based practices, and fostering collaboration. Their historical significance, role in evidence-based medicine, and adaptability to the digital age make them indispensable in the quest for improved healthcare outcomes. As they continue to evolve, medical science journals will play a vital role in shaping the future of medicine and healthcare worldwide.
"journals" refer to academic or professional publications that contain articles and research papers related to various aspects of the medical field. These journals serve as a platform for the dissemination of new medical knowledge, research findings, clinical studies, and expert opinions. They play a crucial role in advancing medical science, sharing best practices, and keeping healthcare professionals, researchers, and students informed about the latest developments in medicine and related disciplines.
Breast cancer classification with histopathological image based on machine le...IJECEIAES
Breast cancer represents one of the most common reasons for death in the worldwide. It has a substantially higher death rate than other types of cancer. Early detection can enhance the chances of receiving proper treatment and survival. In order to address this problem, this work has provided a convolutional neural network (CNN) deep learning (DL) based model on the classification that may be used to differentiate breast cancer histopathology images as benign or malignant. Besides that, five different types of pre-trained CNN architectures have been used to investigate the performance of the model to solve this problem which are the residual neural network-50 (ResNet-50), visual geometry group-19 (VGG-19), Inception-V3, and AlexNet while the ResNet-50 is also functions as a feature extractor to retrieve information from images and passed them to machine learning algorithms, in this case, a random forest (RF) and k-nearest neighbors (KNN) are employed for classification. In this paper, experiments are done using the BreakHis public dataset. As a result, the ResNet-50 network has the highest test accuracy of 97% to classify breast cancer images.
At the 35th AICC-RCOG Annual Conference in association with FOGSI and MOGS, Dr. Niranjan Chavan, President of MOGS, gave an address on Artificial Intelligence in Gynaecologic Oncology at Taj Lands' End, Bandra, Mumbai on the 6th November 2022
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.
The current big challenge facing radiologists in healthcare is the automatic detection and classification of masses in breast mammogram images. In the last few years, many researchers have proposed various solutions to this problem. These solutions are effectively dependent and work on annotated breast image data. But these solutions fail when applied to unlabeled and non-annotated breast image data. Therefore, this paper provides the solution to this problem with the help of a neural network that considers any kind of unlabeled data for its procedure. In this solution, the algorithm automatically extracts tumors in images using a segmentation approach, and after that, the features of the tumor are extracted for further processing. This approach used a double thresholding-based segmentation technique to obtain a perfect location of the tumor region, which was not possible in existing techniques in the literature. The experimental results also show that the proposed algorithm provides better accuracy compared to the accuracy of existing algorithms in the literature.
A Classification of Cancer Diagnostics based on Microarray Gene Expression Pr...IJTET Journal
inAbstract— Pattern Recognition (PR) plays an important role in field of Bioinformatics. PR is concerned with processing raw measurement data by a computer to arrive at a prediction that can be used to formulate a decision to be taken. The important problem in which pattern recognition are applied have common that they are too complex to model explicitly. Diverse methods of this PR are used to analyze, segment and manage the high dimensional microarray gene data for classification. PR is concerned with the development of systems that learn to solve a given problem using a set of instances, each instances represented by a number of features. The microarray expression technologies are possible to monitor the expression levels of thousands of genes simultaneously. The microarrays generated large amount of data has stimulate the development of various computational methods to different biological processes by gene expression profiling. Microarray Gene Expression Profiling (MGEP) is important in Bioinformatics, it yield various high dimensional data used in various clinical applications like cancer diagnostics and drug designing. In this work a new schema has developed for classification of unknown malignant tumors into known class. According to this work an new classification scheme includes the transformation of very high dimensional microarray data into mahalanobis space before classification. The eligibility of the proposed classification scheme has proved to 10 commonly available cancer gene datasets, this contains both the binary and multiclass data sets. To improve the performance of the classification gene selection method is applied to the datasets as a preprocessing and data extraction step.
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.
SVM &GA-CLUSTERING BASED FEATURE SELECTION APPROACH FOR BREAST CANCER DETECTIONijscai
Mortality leading among women in developed countries is breast cancer. Breast cancer is women's second most prominent cause of cancer mortality worldwide. In recent decades, women's high prevalence of breast cancer has risen dramatically. This paper discussed several data analysis methods used to detect breast cancer early. Breast cancer diagnosis distinguishes benign and malignant breast lumps. Using data processing tools, we tackled this disease analysis. Data mining is an important step of library discovery where intelligent methods are used to detect patterns. Several clinical breast cancer studies were conducted using soft computing and machine learning techniques. Sometimes their algorithms are easier, easier, or more comprehensive than others. This research is focused on genetic programming and machine learning algorithms to reliably identify benign and malignant breast cancer. This study aimed to optimise the testing algorithm. We used genetic programming methods to choose classification machines' best features and parameter values. Data mining is an important step of library discovery where intelligent methods are used to detect patterns. We are analysing data accessible from the U.C.I. deep-learning data set in Wisconsin. In this experiment, we equate four Weka clustering strategies with genetic clustering. A comparison of results reveals that sequential minimal optimization (S.M.O.) is better than I.B.K. and B.F. Tree processes, i.e. 97.71%.
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.
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.
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.
USING ARTIFICIAL NEURAL NETWORK IN DIAGNOSIS OF THYROID DISEASE: A CASE STUDYijcsa
Nowadays, one of the main issues to create challenges in medicine sciences by developing technology is the
disease diagnosis with high accuracy. In the recent decades, Artificial Neural Networks (ANNs) are considered as the best solutions to achieve this goal and involve in widespread researches to diagnose the diseases. In this paper, we consider a Multi-layer Perceptron (MLP) ANN using back propagation learning algorithm to classify Thyroid disease. It consists of an input layer with 5 neurons, a hidden layer with 6 neurons and an output layer with just 1 neuron. The suitable selection of activation function and the number of neurons in the hidden layer and also the number of layers are achieved using test and error method. Our simulation results indicate that the performed optimization in MLP ANNs can be reached the accuracy level to 98.6%.
SVM &GA-CLUSTERING BASED FEATURE SELECTION APPROACH FOR BREAST CANCER DETECTIONijscai
Mortality leading among women in developed countries is breast cancer. Breast cancer is women's second most prominent cause of cancer mortality worldwide. In recent decades, women's high prevalence of breast cancer has risen dramatically. This paper discussed several data analysis methods used to detect breast cancer early. Breast cancer diagnosis distinguishes benign and malignant breast lumps. Using data processing tools, we tackled this disease analysis. Data mining is an important step of library discovery where intelligent methods are used to detect patterns. Several clinical breast cancer studies were conducted using soft computing and machine learning techniques. Sometimes their algorithms are easier, easier, or more comprehensive than others. This research is focused on genetic programming and machine
learning algorithms to reliably identify benign and malignant breast cancer. This study aimed to optimise the testing algorithm. We used genetic programming methods to choose classification machines' best features and parameter values. Data mining is an important step of library discovery where intelligent methods are used to detect patterns. We are analysing data accessible from the U.C.I. deep-learning data
set in Wisconsin. In this experiment, we equate four Weka clustering strategies with genetic clustering. A comparison of results reveals that sequential minimal optimization (S.M.O.) is better than I.B.K. and B.F. Tree processes, i.e. 97.71%.
SVM &GA-CLUSTERING BASED FEATURE SELECTION APPROACH FOR BREAST CANCER DETECTIONijscai
Mortality leading among women in developed countries is breast cancer. Breast cancer is women's second
most prominent cause of cancer mortality worldwide. In recent decades, women's high prevalence of breast
cancer has risen dramatically. This paper discussed several data analysis methods used to detect breast
cancer early. Breast cancer diagnosis distinguishes benign and malignant breast lumps. Using data
processing tools, we tackled this disease analysis. Data mining is an important step of library discovery
where intelligent methods are used to detect patterns. Several clinical breast cancer studies were
conducted using soft computing and machine learning techniques. Sometimes their algorithms are easier,
easier, or more comprehensive than others. This research is focused on genetic programming and machine
learning algorithms to reliably identify benign and malignant breast cancer. This study aimed to optimise
the testing algorithm. We used genetic programming methods to choose classification machines' best
features and parameter values. Data mining is an important step of library discovery where intelligent
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BREAST CANCER DIAGNOSIS USING MACHINE LEARNING ALGORITHMS –A SURVEY
1. International Journal of Distributed and Parallel Systems (IJDPS) Vol.4, No.3, May 2013
DOI : 10.5121/ijdps.2013.4309 105
BREAST CANCER DIAGNOSIS USING MACHINE
LEARNING ALGORITHMS –A SURVEY
B.M.Gayathri.1
,C.P.Sumathi2
and T.Santhanam3
1
Department of Computer Science, SDNB Vaishnav College for Women,
Chennai, India
gayathri_bm2003@yahoo.co.in
2
Department of Computer Science, SDNB Vaishnav College for Women,
Chennai, India
drcpsumathi@gmail.com
3
Department of Computer Application, D.G.Vaishnav College for Men, Arumbakkam,
Chennai, India
santhanam_dgvc@yahoo.com
ABSTRACT
Breast cancer has become a common factor now-a-days. Despite the fact, not all general hospitals
have the facilities to diagnose breast cancer through mammograms. Waiting for diagnosing a breast
cancer for a long time may increase the possibility of the cancer spreading. Therefore a computerized
breast cancer diagnosis has been developed to reduce the time taken to diagnose the breast cancer and
reduce the death rate. This paper summarizes the survey on breast cancer diagnosis using various machine
learning algorithms and methods, which are used to improve the accuracy of predicting cancer. This survey
can also help us to know about number of papers that are implemented to diagnose the breast cancer.
KEYWORDS
Neural networks, SVM, RVM, ELM
1. INTRODUCTION
Breast cancer has become one of the most common disease among women that leads to
death. Breast cancer can be diagnosed by classifying tumors. There are two different types of
tumors such as malignant and benign tumors. Physicians need a reliable diagnosis procedure to
distinguish between these tumors. But generally it is very difficult to distinguish tumors even by
the experts. Hence automation of diagnostic system is needed for diagnosing tumors. Many
researchers have attempted to apply machine learning algorithms for detecting survivability of
cancers in human beings and it is also been proved by the researchers that these algorithms work
better in detecting cancer diagnosis. This paper summarizes the application of machine learning
algorithms in detecting cancer in human. In this survey section 2 gives the information of neural
network, its learning rules. Section 3 specifies about literature review based on Artificial Neural
Network (ANN).Section 4 specifies other related works on breast cancer using neural networks.
Section 5 implies with other machine learning algorithms and its types, with related work on
those algorithms.
2. International Journal of Distributed and Parallel Systems (IJDPS) Vol.4, No.3, May 2013
106
1.1 Machine learning Algorithms
Machine learning, a branch of artificial intelligence, is a scientific discipline concerned
with the design and development of algorithms that allow computers to evolve behaviors based
on empirical data, such as from sensor data or databases.
1.2 Types of Machine Learning Algorithms
Supervised learning.
Unsupervised learning.
Semi-supervised learning.
Reinforcement learning.
Transduction .
Learning to learn.
2. NEURAL NETWORKS AND ITS LEARNING RULES:
A neural network is a model that is designed by the way human nervous systems such as
brain, that process the information. Neural networks, with their remarkable ability to derive
meaning from complicated or imprecise data, can be used to extract patterns and detect trends that
are too complex to be noticed by either humans or other computer techniques. Many neural
network models, even biological neural networks assume many simplifications over actual
biological neural networks.
Such simplifications are necessary to understand the intended properties and to attempt any
mathematical analysis. Even if all the properties of the neurons are known, simplification is still
needed for analytical purpose. Neural networks are adaptive statistical devices. This means that
they can change (synaptic weights) as a function of their performance. In ANNs, all the neurons
are operating at the same time, which makes ANN to perform tasks at much faster rate.
Table 1. Performance of neural networks algorithms for detecting breast cancer
S.no Year Author Algorithms/Techniques
used
Result
obtained
1. 2010 F.Paulin
,Dr.A.Santhakumaran
Back propagation
algorithm used for
training Multilayer
Perceptron(MLP)
99.28%
2. 2010 Dr.K .Usha rani Feed forward, Back
propagation.
92%
3. 2011 F.Paulin,
Dr.A.Santhakumaran
Back propagation, Quasi
newton, levennberg
Marquardt algorithm.
99.28%
4. 2011 Yao ying huang,wang
sen li ,Xiaojiao ye
Genetic algorithm, feature
selection
99.1%
.
3. International Journal of Distributed and Parallel Systems (IJDPS) Vol.4, No.3, May 2013
107
3. RELATED WORKS FOR BREAST CANCER DIAGNOSIS USING NEURAL
NETWORKS
Tuba kiyan [2] et al. 2004 has discussed that statistical neural networks can be used to
perform breast cancer diagnosis effectively. The scholar has compared statistical neural network
with Multi Layer Perceptron on WBCD database. Radial basis function(RBF), General
Regression Neural Network(GRNN),Probabilistic Neural Network(PNN) were used for
classification and their overall performance were 96.18% for Radial Basis Function (RBF), 97%
PNN, 98.8% for GRNN and 95.74% for MLP. Hence it is proved that these statistical neural
network structures can be applied to diagnose breast cancer.
Xin Yao [24] et al. 1999 has attempted to implement neural network for breast cancer
diagnosis. Negative correlation training algorithm was used to decompose a problem
automatically and solve them. In this article the author has discussed two approaches such as
evolutionary approach and ensemble approach, in which evolutionary approach can be used to
design compact neural network automatically. The ensemble approach was aimed to tackle large
problems but it was in progress.
Dr.S.Santhosh baboo and S.Sasikala [27] have done a survey on data mining techniques
for gene selection classification. This article dealt with most used data mining techniques for gene
selection and cancer classification, particularly they have focused on four main emerging fields.
They are neural network based algorithms, machine learning algorithms, genetic algorithm and
cluster based algorithms and they have specified future improvement in this field.
Afzan Adam[28] et al. have developed a computerized breast cancer diagnosis by
combining genetic algorithm and Back propagation neural network which was developed as faster
classifier model to reduce the diagnose time as well as increasing the accuracy in classifying mass
in breast to either benign or malignant. In these two different cleaning processes was carried out
on the dataset. In Set A, it only eliminated records with missing values, while set B was trained
with normal statistical cleaning process to identify any noisy or missing values. At last Set A gave
100% of highest accuracy percentage and set B gave 83.36% of accuracy. Hence the author has
concluded that medical data are best kept in its original value as it gives high accuracy percentage
as compared to altered data.
David B.fogel [26] et al. has discussed the evolving neural networks for detecting breast
cancer and the related works used for breast cancer diagnosis using back propagation method
with multilayer perceptron. In contrast to back propagation David B.fogel et al. found that
evolution computational method and algorithms were used often, outperform more classic
optimization techniques.
The author has applied 699 data, which has missing values and removed, leaving 683
data. Using these values two experimental designs were conducted. The first experiment
consisted of five trials with 9-2-1 Multi Layer Perceptron (i.e., 9 input, 2 hidden nodes, and 1
output node) and second experiment consisted of 9-9-1 Multi Layer Perceptron. The result of the
first experiment after 400 generations in each five trials had accuracy of 97.5%. In second
experiment, in comparison with previous experiment, best performance reported with an accuracy
rate of 98.2% for lesser hidden nodes.
A.Punitha [15] et al. 2007 have discussed the genetic algorithm and adaptive resonance
theory neural network for breast cancer diagnosis using Wisconsin Breast Cancer Data (WBCD).
They trained 699 samples which was taken from Fine Needle Aspirates (FNA) with 16 missing
4. International Journal of Distributed and Parallel Systems (IJDPS) Vol.4, No.3, May 2013
108
data, and 683 samples with breast tumors are used in this work of which 65% was proved to be
benign and 35% malignant. The author has also compared the result of Adaptive Resonance
Theory (ART) with Radial Basis Function (RBF), Probabilistic Neural Network (PNN), Multi
Layer Perceptron (MLP), in which the performance of these combined approach has not only
improved the accuracy but also reduced the time taken to train the network.
Val´erie Bourd`es [5] et al., 2010 have submitted the article by comparing artificial neural
network with logistic regression. The author has compared multilayer perceptron Neural
Networks (NNs) with Standard Logistic Regression (SLR) to identify key covariates impacting
on mortality from cancer causes, Disease-Free Survival (DFS), and Disease Recurrence using
Area Under Receiver-Operating Characteristics (AUROC) in breast cancer patients.
From 1996 to 2004, 2,535 patients diagnosed with primary breast cancer entered into the
study at a single French centre, where they received standard treatment. For specific mortality as
well as DFS analysis, the Receiver-Operating Characteristics (ROC) curves were greater with the
NN models compared to LR model with better sensitivity and specificity. Four predictive factors
were retained by both approaches for mortality: clinical size stage, Scarff Bloom Richardson
grade, number of invaded nodes, and progesterone receptor. The results enhanced the relevance
of the use of NN models in predictive analysis in oncology, which appeared to be more accurate
in prediction in this French breast cancer cohort.
Chih-Lin Chi [4] et al., 2007 have presented an article on survival analysis of breast cancer on
two breast cancer datasets. This article applies an Artificial Neural Networks (ANNs) to the
survival analysis problem. Because ANNs can easily consider variable interactions and create a
non-linear prediction model, they offer more flexible prediction of survival time than traditional
methods. This study compares ANN results on two different breast cancer datasets, both of which
use nuclear morphometric features. The results show that ANNs can successfully predict
recurrence probability and separate patients with good and bad prognosis.
Figure 1.percentage of articles presented for diagnosing breast cancer using neural networks
From this survey, it can be found that there are many articles based on breast cancer
diagnosis, which have been presented by many researchers and they are still undergoing research
on developing more algorithms to get more accuracy for detecting breast cancer.
Now-a-days machine learning algorithms are mostly used for detecting the cancer disease. In
the following section let us see, about other machine learning algorithms and some of the papers
presented based on it.
5. International Journal of Distributed and Parallel Systems (IJDPS) Vol.4, No.3, May 2013
109
4. OTHER MACHINE LEARNING ALGORITHMS
Some of the other machine learning algorithms are Support Vector Machine, Relevance
Vector Machine. The following section gives the brief information about these learning
algorithms.
4.1 SUPPORT VECTOR MACHINE
A support vector machine (SVM) is a concept in statistics and computer science for a set of
related supervised learning methods that analyze data and recognize patterns, used
for classification and regression analysis. The standard SVM takes a set of input data and
predicts, for each given input, which of two possible classes forms the input, making the SVM a
non-probabilistic binary linear classifier.
Table 2. List Performance of breast cancer diagnosis using SVM
S.no Year Author Algorithms Result
obtained
1. 2012 JR Marsilin SVM 78%
2. 2011 Li Rong,Sunyuan SVM-KNN classifier 98.06%
3. 2011 F Eddaoudi SVM 95%
4. 2011 S. Aruna,
Dr S.P. Rajagopalan
SVM 98.24%
Table 2 shows that SVM technique is used for detecting breast cancer. But Relevance
vector machine (RVM) gives more accurate results than support vector machines. This has been
proved by applying RVM in other cancer diagnosis such as ovarian cancer, optical cancer and
general cancer classifications. Hence Relevance vector machine can also be applied to attain best
result for diagnosing breast cancer.
4.1.1 RELATED WORKS ON DIAGNOSING BREAST CANCER USING SVM:
Ilias Maglogiannis [9] et al. 2009 have presented an article on An intelligent system for
automated breast cancer diagnosis & prognosis using SVM based classifiers with Bayesian
classifiers and ANN for prognosis & diagnosis of breast cancer disease. Wisconsin diagnostic
breast cancer datasets were used to implement SVM model to provide distinction between the
malignant & benign breast masses. These datasets involve measurement taken according to Fine
Needle Aspirates (FNA). The article provides the implementation details along with the
corresponding results for all the assessed classifiers. Several comparative studies have been
carried out concerning both the prognosis and diagnosis problem demonstrating the superiority of
the proposed SVM algorithm in terms of sensitivity, specificity and accuracy.
6. International Journal of Distributed and Parallel Systems (IJDPS) Vol.4, No.3, May 2013
110
Y.Iraneus Anna Rejani and Dr.S.Thamarai selvi [12] 2009 have presented an article on
Early detection of breast cancer using SVM classifier technique. In this article the authors have
discussed, how to detect tumor from mammograms. In this article the authors have specified an
algorithm for tumor detection and have proposed the method that includes the mammograms
image, which were filtered with Gaussian filter based on standard deviation and matrix
dimensions such as rows and columns.
Then the filtered image was used for contrast stretching. The background image is
eliminated using Top hat operation. The top hat output is decomposed and reconstructed using
Discrete Wavelet Transform (DWT). The reconstructed image is used for segmentation.
Thresholding method was used for segmentation and then the features were extracted from the
tumor area. This method can be summarized as the initial step based on gray level information of
image enhancement. For each tumor region extract, morphological features were extracted to
categorize the breast tumor and finally SVM classifiers were used for classification.
Z.Qinli [29] et al. has presented an article on, a approach to SVM and its application to
breast cancer diagnosis. In this article, the authors have proposed a method for improving the
performance of SVM classifier by modifying kernel functions. This is based on the differential
approximation of metric. The method is to enlarge margin around separating hyper plane by
modifying the kernel functions using a
Figure 2. Percentage of articles presented for breast cancer diagnosis using SVM
positive scalar functions so that the seperability is increased. In this article, the author have
specified specifically for modifying Gaussian Radial Basis function kernel. The result for both
artificial and real data, show remarkable improvement of generalization error and computational
cost.
4.2 RELEVANCE VECTOR MACHINE
Relevance vector machine (RVM) is a machine learning technique that uses Bayesian
inference to obtain parsimonious solutions for regression and classification. The RVM has an
identical functional form to the support vector machine, but provides probabilistic classification.
It is actually equivalent to a Gaussian process model with covariance function:
where φ is the kernel function (usually Gaussian), and x1,…,xN are the input vectors of
the training set.
2007
2009
2010
2011
2012
0
50
100
percenta
ge
year
7. International Journal of Distributed and Parallel Systems (IJDPS) Vol.4, No.3, May 2013
111
4.2.1 CASE STUDY ON SOME OF THE PAPERS PRESENTED FOR DETECTING
CANCERS USING RVM:
Compared to that of support vector machines (SVM), the Bayesian formulation of the
RVM avoids the set of free parameters of the SVM (that usually require cross-validation-based
post-optimizations). However RVMs use expectation maximization (EM)-like learning method
and are therefore at risk of local minima. This is unlike the standard Sequential Minimal
Optimization (SMO)-based algorithms employed by SVMs, which are guaranteed to find a global
optimum.RVM is used by many authors for detecting cancer in human beings.For detecting
cancer such as ovarian cancers,optical cancers etc., relevance vector machine is used, which has
been proved to give more accurate results than support vector machine.
S.no Year Author Algorithms/Techniques
used
1. 2002 Balaji Krishnapuram,Lawrence
Carin,Alexander J. Hartemink
RVM(linear kernel)
2. 2003 Balaji Krishnapuram,Lawrence
Carin,Alexander J. Hartemink
RVM
3. 2004 Balaji Krishnapuram,Lawrence
Carin,Alexander J. Hartemink
RVM
4. 2005 Shovan K. Majumder RVM
5. 2005 L Wei, Y Yang, RM Nishikawa RVM
6. 2007 Wen Zhang Liu, J RVM
7. 2009 S Ozer, MA Haider, DL Langer RVM
Table 3. Literature review on RVM
From the above tabular column it can be found that RVM is applied for detecting cancers
such as prostate cancer,optical cancer etc.,.Since RVM gives the result more accurate than SVM
it can be applied for detecting breast cancer also.
5. CONCLUSION
In this survey , the performance of different machine learning algorithms such as Support
Vector Machine(SVM) and Relevance Vector Machine(RVM) are assessed. Many researchers
have applied the algorithm of neural networks for predicting cancers,especially the breast cancer.
By going through various articles,RVM is applied for detecting optical cancer,ovarian cancer
etc.Overall, if studies on RVM continues,then it is likely that the use of RVM will become much
more useful in diagnosing breast cancer.
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