Classification of mammograms for breast cancer detection based on curvelet transform and multi-layer perceptron

In this paper, classification of mammograms for breast cancer detection based on Discrete Curvelet Transform (DCT) and Multi-Layer Perceptron (MLP) is proposed. The mammogram patches are first filtered by Column wise neighborhood operations Filter (COLFILT). Enhanced patches are further decomposed into four sub-bands by using DCT. Dense Scale Invariant Feature Transform (DSIFT) method is use to extract the six rotation and scale invariant features for all the sub-bands. By using these sub-bands of all the patches, a feature matrix is created that is further processed by MLP for classification. The proposed method is tested using the Image Retrieval in Medical Application (IRMA) dataset. Numerical validation results and graph shows the significance of proposed scheme as compared to state of art existing schemes.