Design, analysis and classifier evaluation for a CAD tool for breast cancer detection from digital mammograms

In this paper, the design, analysis, and classifier evaluation for a computer aided diagnostics (CAD) tool for early breast cancer detection from mammograms is presented. The design steps of a CAD tool include enhancement, segmentation, feature extraction and selection, and classification of images. A contrast limited histogram equalisation method is used for image enhancement followed by cropping of region of interests. The fuzzy C-means method is used for segmenting abnormalities present in the images. A total of 88 hybrid features are extracted for each image. For feature selection, minimum redundancy and maximum relevancy approach has been used. For decision making, the various classifiers examined for their efficacy, for 322 images available in MIAS database, include SVMs for its various kernel choices, k-NN, and ANN. Finally, it is observed that the SVM classifier for the MLP kernel choice is performing better in comparison to all other classifiers in consideration along with the other design steps as above.