Abstract
Mammogram - breast x-ray imaging - is considered the most effective, low cost, and reliable method in early detection of breast cancer. Clustered Microcalcifications are an important early sign of breast cancer. In this paper, we are introducing, as an aid to radiologists, a computer-aided diagnosis (CAD) system, which could be helpful in detecting microcalcifications faster than traditional screening program without the drawback attribute to human factors. The techniques used in this paper for feature extraction is based on the fractal modeling of locally processed image (ROI). Classification between normal and microcalcification is done using the voting K-Nearest Neighbor classifier and the support vector machine classifier. The two classification techniques used were compared through the system to reach a better classification decision.