scholarly journals An Efficient Segmentation and Classification System in Medical Images Using Intuitionist Possibilistic Fuzzy C-Mean Clustering and Fuzzy SVM Algorithm

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 3903 ◽  
Author(s):  
Chiranji Lal Chowdhary ◽  
Mohit Mittal ◽  
Kumaresan P. ◽  
P. A. Pattanaik ◽  
Zbigniew Marszalek
Keyword(s):  
Breast Cancer ◽  
Classification System ◽  
Medical Images ◽  
Research Work ◽  
Support Vector ◽  
Clustering Methods ◽  
Analysis Society ◽  
Fuzzy Clustering Methods ◽  
Detection And Diagnosis

The herpesvirus, polyomavirus, papillomavirus, and retrovirus families are associated with breast cancer. More effort is needed to assess the role of these viruses in the detection and diagnosis of breast cancer cases in women. The aim of this paper is to propose an efficient segmentation and classification system in the Mammography Image Analysis Society (MIAS) images of medical images. Segmentation became challenging for medical images because they are not illuminated in the correct way. The role of segmentation is essential in concern with detecting syndromes in human. This research work is on the segmentation of medical images based on intuitionistic possibilistic fuzzy c-mean (IPFCM) clustering. Intuitionist fuzzy c-mean (IFCM) and possibilistic fuzzy c-mean (PFCM) algorithms are hybridised to deal with problems of fuzzy c-mean. The introduced clustering methodology, in this article, retains the positive points of PFCM which helps to overcome the problem of the coincident clusters, thus the noise and less sensitivity to the outlier. The IPFCM improves the fundamentals of fuzzy c-mean by using intuitionist fuzzy sets. For the clustering of mammogram images for breast cancer detector of abnormal images, IPFCM technique has been applied. The proposed method has been compared with other available fuzzy clustering methods to prove the efficacy of the proposed approach. We compared support vector machine (SVM), decision tree (DT), rough set data analysis (RSDA) and Fuzzy-SVM classification algorithms for achieving an optimal classification result. The outcomes of the studies show that the proposed approach is highly effective with clustering and also with classification of breast cancer. The performance average segmentation accuracy for MIAS images with different noise level 5%, 7% and 9% of IPFCM is 91.25%, 87.50% and 85.30% accordingly. The average classification accuracy rates of the methods (Otsu, Fuzzy c-mean, IFCM, PFCM and IPFCM) for Fuzzy-SVM are 79.69%, 92.19%, 93.13%, 95.00%, and 98.85%, respectively.

Survey of Breast Cancer Detection Using Machine Learning Techniques in Big Data

2019 ◽  
Vol 21 (3) ◽  
pp. 80-92
Author(s):  
Madhuri Gupta ◽  
Bharat Gupta
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Research Work ◽  
Cancer Recurrence ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Common Disease ◽  
Learning Techniques ◽  
Learning Frameworks ◽  
Processing Engine

Cancer is a disease in which cells in body grow and divide beyond the control. Breast cancer is the second most common disease after lung cancer in women. Incredible advances in health sciences and biotechnology have prompted a huge amount of gene expression and clinical data. Machine learning techniques are improving the prior detection of breast cancer from this data. The research work carried out focuses on the application of machine learning methods, data analytic techniques, tools, and frameworks in the field of breast cancer research with respect to cancer survivability, cancer recurrence, cancer prediction and detection. Some of the widely used machine learning techniques used for detection of breast cancer are support vector machine and artificial neural network. Apache Spark data processing engine is found to be compatible with most of the machine learning frameworks.

scholarly journals AN IN-DEPTH ANALYSIS OF THE IDENTIFIED ALGORITHMS AND THEIR COMPARATIVE STUDY IN THE EARLY DETECTION AND DIAGNOSIS OF BREAST CANCER

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Mridul Sharma
Keyword(s):  
Breast Cancer ◽  
Artificial Intelligence ◽  
Early Detection ◽  
Healthcare Workers ◽  
State Of The Art ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Depth Analysis ◽  
Detection And Diagnosis ◽  
Early Detection And Diagnosis

These days one of the major inevitable ailments for females is bosom malignancy. The appropriate medication and early findings are important stages to take to thwart this ailment. Although, it's not easy to recognize due to its few vulnerabilities and lack of data. Can use artificial intelligence to create devices that can help doctors and healthcare workers to early detection of this cancer. In This research, we investigate three specific machine learning algorithms widely used to detect bosom ailments in the breast region. These algorithms are Support vector machine (SVM), Bayesian Networks (BN) and Random Forest (RF). The output in this research is based on the State-of-the-art technique.

Computer-Aided Detection and Diagnosis of Breast Cancer Using Machine Learning, Texture and Shape Features

2010 ◽  
pp. 27-53
Author(s):  
Geraldo Braz Júnior ◽  
Leonardo de Oliveira Martins ◽  
Aristófanes Corrêa Silva ◽  
Anselmo Cardoso de Paiva
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Computer Aided Detection ◽  
Learning Techniques ◽  
Computer Aided ◽  
Cause Of Deaths ◽  
Cancer Tumor ◽  
Detection And Diagnosis

Breast cancer is a malignant (cancer) tumor that starts from cells of the breast, being the major cause of deaths by cancer in the female population. There has been tremendous interest in the use of image processing and analysis techniques for computer aided detection (CAD)/ diagnostics (CADx) in digital mammograms. The goal has been to increase diagnostic accuracy as well as the reproducibility of mammographic interpretation. CAD/CADx systems can aid radiologists by providing a second opinion and may be used in the first stage of examination in the near future, providing the reduction of the variability among radiologists in the interpretation of mammograms. This chapter provides an overview of techniques used in computer-aided detection and diagnosis of breast cancer. The authors focus on the application of texture and shape tissues signature used with machine learning techniques, like support vector machines (SVM) and growing neural gas (GNG).

Breast cancer analysis using Machine Learning algorithms

2021 ◽  
Vol 2 (1) ◽  
pp. 12-16
Author(s):  
Lina Alkhathlan ◽  
Abdul Khader Jilani Saudagar
Keyword(s):  
Breast Cancer ◽  
Breast Tissue ◽  
Causes Of Death ◽  
State Of The Art ◽  
Early Stage ◽  
Research Work ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Risk Of Death ◽  
Breast Cells

Breast cancer (BC) is one of the most common types of cancer and one of the leading causes of death for women around the world. Breast cancer occurs when cells in the breast cells mutate and form a malignant tumor. State-of-the-art technologies can detect BC at an early stage, which helps in treatment and reduces the risk of death. Medical doctors commonly use breast tissue biopsy when diagnosing breast cancer, enabling them to take a microscopic look for breast tissue and determine whether the tissue is benign or malignant. To improve biopsy results, many researchers have studied the feasibility of using artificial intelligence (AI) to help doctors detect any harmful changes that may lead to cancer. In this research work detail analysis of Breast Cancer using support vector machine (SVM) and convolutional neural networks (CNN) algorithms is performed and the results show CNN has more superior results in comparison to SVM in the recognition of images affected by Breast Cancer.

Computer-Aided Detection and Diagnosis of Breast Cancer Using Machine Learning, Texture and Shape Features

2012 ◽  
pp. 769-792
Author(s):  
Geraldo Braz Júnior ◽  
Leonardo de Oliveira Martins ◽  
Aristófanes Corrêa Silva ◽  
Anselmo Cardoso de Paiva
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Computer Aided Detection ◽  
Learning Techniques ◽  
Computer Aided ◽  
Cause Of Deaths ◽  
Cancer Tumor ◽  
Detection And Diagnosis

Breast cancer is a malignant (cancer) tumor that starts from cells of the breast, being the major cause of deaths by cancer in the female population. There has been tremendous interest in the use of image processing and analysis techniques for computer aided detection (CAD)/ diagnostics (CADx) in digital mammograms. The goal has been to increase diagnostic accuracy as well as the reproducibility of mammographic interpretation. CAD/CADx systems can aid radiologists by providing a second opinion and may be used in the first stage of examination in the near future, providing the reduction of the variability among radiologists in the interpretation of mammograms. This chapter provides an overview of techniques used in computer-aided detection and diagnosis of breast cancer. The authors focus on the application of texture and shape tissues signature used with machine learning techniques, like support vector machines (SVM) and growing neural gas (GNG).

scholarly journals An optimized SVM kernel for texture classification and its application in microcalcification detection

2021 ◽  
Author(s):  
Mahdi Sabri
Keyword(s):  
Breast Cancer ◽  
Feature Extraction ◽  
Medical Images ◽  
Texture Classification ◽  
Dimensional Space ◽  
Good Choice ◽  
Support Vector ◽  
Classification Problems ◽  
Diagnose Breast Cancer ◽  
Cad Systems

Mammograms, commonly used to diagnose breast cancer, are difficult medical images to interpret. Computer aided diagnosis (CAD) systems have the potential to assist radiologists by locating suspicious regions in the mammograms for more detailed examination. One approach is for CAD systems to detect microcalcification. This approach uses classification of texture features and has applications for the detection of breast cancer as well as other abnormalties in medical images. The Support Vector Machine (SVM) has been shown to be effective in texture classification. SVM performs well in high dimensional space such as the space spanned by texture images. The kernel function in SVM algorithm implicitly performs feature extraction. Since SVM is basically suited for two-class classification problems, it is potentially a good choice for several different medical imaging which deal with abnormality detection. The main contribution of this thesis in the sense of texture classification is proposing a new texture classification algorithm by effectively employing external features within SVM kernel and introducing a new feature extraction method for texture classification.

scholarly journals Comparison of Classification Models for Breast Cancer Identification using Google Colab

2020 ◽  
Author(s):  
SUNDARAMBAL BALARAMAN
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Logistic Regression ◽  
Learning Algorithms ◽  
Research Work ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Classification Algorithms ◽  
Classification Models ◽  
K Nearest Neighbor

Classification algorithms are very widely used algorithms for the study of various categories of data located in multiple databases that have real-world implementations. The main purpose of this research work is to identify the efficiency of classification algorithms in the study of breast cancer analysis. Mortality rate of women increases due to frequent cases of breast cancer. The conventional method of diagnosing breast cancer is time consuming and hence research works are being carried out in multiple dimensions to address this issue. In this research work, Google colab, an excellent environment for Python coders, is used as a tool to implement machine learning algorithms for predicting the type of cancer. The performance of machine learning algorithms is analyzed based on the accuracy obtained from various classification models such as logistic regression, K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Naïve Bayes, Decision Tree and Random forest. Experiments show that these classifiers work well for the classification of breast cancers with accuracy>90% and the logistic regression stood top with an accuracy of 98.5%. Also implementation using Google colab made the task very easier without spending hours of installation of environment and supporting libraries which we used to do earlier.

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