Automated Mass Detection from Mammograms using Deep Learning and Random Forest

Mass detection from mammogram plays an crucial role as a pre-processing stage for mass segmentation and classification. In this paper, we present a novel approach for detecting masses from mammograms using a cascade of deep learning and random forest classifiers. The deep learning classifier consists of a multi-scale deep belief network classifier that selects regions to be further processed by a two-level cascade of deep convolutional neural networks. The regions that survive this deep learning analysis is then processed by a two-level cascade of random forest classifiers that use several morphological and texture features extracted from those surviving regions. We show that the proposed cascade of deep learning and random forest classifiers are effective in the reduction of false positive regions, while keeping a high true positive detection, and that the final mass detection produced by our approach achieves the best results in the field on public mammogram datasets.