  Breast cancer is one of the leading causes of death among women across the
globe. It is difficult to treat if detected at advanced stages, however, early
detection can significantly increase chances of survival and improves lives of
millions of women. Given the widespread prevalence of breast cancer, it is of
utmost importance for the research community to come up with the framework for
early detection, classification and diagnosis. Artificial intelligence research
community in coordination with medical practitioners are developing such
frameworks to automate the task of detection. With the surge in research
activities coupled with availability of large datasets and enhanced
computational powers, it expected that AI framework results will help even more
clinicians in making correct predictions. In this article, a novel framework
for classification of breast cancer using mammograms is proposed. The proposed
framework combines robust features extracted from novel Convolutional Neural
Network (CNN) features with handcrafted features including HOG (Histogram of
Oriented Gradients) and LBP (Local Binary Pattern). The obtained results on
CBIS-DDSM dataset exceed state of the art.
