Publications

Abstract : Breast cancer is considered the most serious cancer in women, among other prevalent cancer types. Chances of survival can be significantly increased through early detection, for which mammogram is considered to be the gold standard. This work addresses the development of a computer-aided diagnosis (CAD) system for analysis of mammographic masses. However, different mammographic tissue densities exhibit different characteristics and present abnormalities diversely. This renders a unified CAD framework for evaluation of masses, ineffective. To this end, we propose an ensemble framework for mass characterization, comprising different experts each specialized for a particular tissue category. Specifically, three segmentation-free feature descriptors including local binary pattern (LBP), scale-invariant feature transform (SIFT) and gray-level co-occurrence matrix (GLCM) are extracted from the regions of interest (ROIs), followed by individual classification with each feature descriptor using four different learning models, viz. support vector machine (SVM), artificial neural network (ANN), random forest (RF) and extreme grading boosting machine (XG Boost). All these 12 combinations are explored for each of the four breast density categories separately, to determine the best feature–classifier combination for a given category. The proposed ensemble scheme was validated on 1057 suspicious regions from digital database for screening mammograms (DDSM), demonstrating an improved performance when compared to state-of-the-art single learning framework modeled on all density categories collectively.