Breast cancer is one of the leading causes of death among the women. Mammogram analysis is the most effective method that helps in the early detection of breast cancer. Microcalcification, masses, and architectural detection in the mammogram plays an important role in the later stages of diagnosis. In this paper we propose an effective method for the detection and classification of clustered microcalcification. We applied the proposed method in the MIAS datasets and found the effectiveness in the detection and classification of clustered microcalcification. We also brief out in this article the methods adopted to select the features for clustered microcalcification and technique to handle the class imbalance specific to microcalcification classification problem.

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Classification Clustered Microcalcification Imbalanced Data Sets Mammography