JPR_V3_N1_RP2 Manoharan R. Kalaimagal R. Journal on Pattern Recognition 2350-112X 3 1 7 15 Computer Aided Detection (CAD), Discrete Shearlet Transform (DST), Nonlinear Support Vector Machine Classifier (NLSVM), Kernel Principal Component Analysis (KPCA) The interpretation and analysis of medical images represent an important and exciting part of computer vision and pattern recognition. Developing a computer-aided diagnosis system for cancer diseases, such as breast cancer, to assist physicians in hospitals is becoming of high importance and priority for many researchers and clinical centers. It is a complex process to develop a computer vision system to perform such tasks. Breast cancer is the cause of the most common cancer death in women. X-ray mammography is widely used to screen women with an increased risk of breast cancer. Computer Aided Detection (CAD) systems have been developed to boost efficiency and accuracy in diagnosing cancer. This research presents the design of CAD for cancerous micro calcification classification in digital mammogram images based on Discrete Shearlet Transform (DST) and Kernel Principal Component Analysis (KPCA). The purpose of the Kernel Principle Component Analysis improved the classification accuracy by reducing the number of features. The implementation of Mammogram breast cancer detection is done using MIAS Database and MATLAB Tool. Results are shown in DST based NLSVM superior to the other conventional classifier techniques. March - May 2016 Copyright © 2016 i-manager publications. All rights reserved. i-manager Publications http://www.imanagerpublications.com/Article.aspx?ArticleId=8103