  Identification and segmentation of breast masses in mammograms face complex
challenges, owing to the highly variable nature of malignant densities with
regards 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 paper
proposes a rigorous segmentation method, supported by morphological enhancement
using grayscale linear filters. A novel cascaded ensemble of support vector
machines (SVM) is used to effectively tackle the class imbalance and provide
significant 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.
