FuzzyDeepNets based feature extraction for classification of mammograms

Breast cancer is one of the most aggressive tumors that claims the lives of women each year. Radiologists recommend mammography to detect cancer at the early stages. Masses, micro-calcifications, and distortion in mammography indicate breast cancer. This paper proposes FuzzyDeepNets for extracting the features and the Hanman transform classifier for the classification of mammograms. In this work, mammograms are categorized based on abnormality present, type of abnormality, and the characteristics of the abnormality present. FuzzyDeepNets allows us to skip the layers thereby reducing the computational complexity of the deep learning architectures. Principal component analysis helps in reducing the dimensionality of the selected features. The results achieved using proposed method on publicly available mini-MIAS, DDSM, INbreast and private database surpasses the results of the state-of-the-art techniques used for comparison. Results of the proposed method are clinically relevant as they are validated by expert radiologists.