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Building Accurate Classifier for the Classification of Microcalcification

The most common life threatening type of cancer affecting woman is breast cancer. Mammography is an effective screening tool for breast cancer. For mammogram the CAD system is like a spell checker. CAD systems use digital image processing techniques to improve the detection performance and efficiency of mammography screening. The two most common features that are associated with cancers are clusters of microcalcifications and masses. There are some reasons such as small size of microcalcification, less brightness of microcalcification compared to the background, and superimposition of microcalcification on textures make the detection of microcalcification difficult. This paper proposes a methodology for the classification of microcalcification in mammograms. An improved classifier that introduces balanced learning for the accurate classification for the classification of microcalcification is proposed as one of the main steps in the methodology. The experiments are conducted on the samples collected from well known MIAS database and outperforms other methods in the classification of microcalcification. Keywords— CAD System, Classification, Imbalanced data, Mammography, Microcalcification