Journal Title : International Journal of Modern Trends in Engineering and Science

Author’s Name : G.G.Udaya, J.Preethi

Volume 01 Issue o6   Year 2014  

ISSN no:  2348-3121 

Page no: 1-5

Abstract— Breast cancer is the second leading cause of mortality among women. Over the past 50 years, it has become a  major health issue in the world and its rate has increased in recent years. Early detection is the most effective way to diagnose and handle breast cancer. Computer-aided detection or diagnosis (CAD) systems can play a key role in the early detection of breast cancer and can reduce the death rate among women with breast cancer. The CAD system consists of preprocessing, detection and classification of microcalcification. Preprocessing of mammograms is done to improve the quality of the image and in separating the pectoral muscle from the whole breast area. Detection of microcalcification is done by segmentation. In this Particle Swarm Optimization (PSO) has been proposed to segment the suspicious regions from the enhanced image. Classification is done by three steps namely feature extraction, feature selection and by classification. Support Vector Machine (SVM) is used as a classifier, which classifies the microcalcification into benign and malignant. SVM improves classification accuracy and proved to possess the best recognition ability due to its ability to deal with nonlinearly separable data sets.

Keywords— Breast cancer, mammogram, pectoral muscle suppression, computer aided detection, microcalcification, segmentation.


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