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Identification and segmentation of breast masses in mammograms face complexchallenges, owing to the highly variable nature of malignant densities withregards to their shape, contours, texture and orientation. Additionally,classifiers typically suffer from high class imbalance in region candidates,where normal tissue regions vastly outnumber malignant masses. This paperproposes a rigorous segmentation method, supported by morphological enhancementusing grayscale linear filters. A novel cascaded ensemble of support vectormachines (SVM) is used to effectively tackle the class imbalance and providesignificant predictions. For True Positive Rate (TPR) of 0.35, 0.69 and 0.82,the system generates only 0.1, 0.5 and 1.0 False Positives/Image (FPI),respectively.

Leveraging SLIC Superpixel Segmentation and Cascaded Ensemble SVM for Fully Automated Mass Detection In Mammograms