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Abstract

Breast cancer detection is an emerging need in mammography and it helps for radiologist for examining the stages of breast cancer detection. Mammogram classification is attempted in this paper using well-known support vector classification method. Mammogram classification follows three key steps, which are feature image enhancement, texture feature extraction, and classification. This paper presents the experimental results of mammogram classification for demonstrating the efficiency of SVM with underlying mechanisms of texture methods and it suggest the best combination of SVM and texture method to radiologist for better medical diagnosis of breast cancer detection.