COMPUTER AIDED DIAGNOSIS IN DIGITAL MAMMOGRAPHY USING COMBINED SUPPORT VECTOR MACHINE AND LINEAR DISCRIMINANT ANALYASIS CLASSIFICATION

This paper presents a computer-aided diagnosis (CAD) system based on combined support vector machine (SVM) and linear discriminant analysis (LDA) classifier for detection and classification breast cancer in digital mammograms. The proposed system has been implemented in four stages: (a) Region of interest (ROI) selection of 32x32 pixels size which identifies suspicion regions. (b) Feature extraction stage locally processed image (ROI) to compute the important features of each breast cancer. (c) Feature selection stage by using forward stepwise linear regression method (FSLR). (d) Classification stage, which classify between normal and abnormal patterns and then classify between benign and malignant abnormal. In classification stage, a new method was used, based on combined SVM and LDA classifier (SVM/LDA), and compared to other classifiers such as SVM, LDA, and fuzzy C-mean (FCM) classifiers. The proposed system was shown to have a large potential for breast cancer diagnostic in digital mammograms.