  Breast cancer is the second leading cause for death among women and it is
diagnosed with the help of mammograms. Oncologists are miserably failed in
identifying the micro calcification at the early stage with the help of the
mammogram visually. In order to improve the performance of the breast cancer
screening, most of the researchers have proposed Computer Aided Diagnosis using
image processing. In this study mammograms are preprocessed and features are
extracted, then the abnormality is identified through the classification. If
all the extracted features are used, most of the cases are misidentified. Hence
feature selection procedure is sought. In this paper, Fuzzy-Rough feature
selection with {\pi} membership function is proposed. The selected features are
used to classify the abnormalities with help of Ant-Miner and Weka tools. The
experimental analysis shows that the proposed method improves the mammograms
classification accuracy.
