Automatic Mass Classification in Breast Using Transfer Learning of Deep Convolutional Neural Network and Support Vector Machine

Mammography is the most widely used gold standard for screening breast cancer, where mass classification is a prominent step. Classification of mass in the breast is, however, an arduous problem as they usually have large variations in terms of shape, size, boundary, and texture. In this study, the process of mass classification is automated with the use of transfer learning of Deep Convolutional Neural Networks (DCNN) to extract features, the bagged decision tree for feature selection, and finally a Support Vector Machine (SVM) classifier for classifying the mass and non-mass tissue. Area Under ROC Curve (AUC) is chosen as the performance metric, which is then maximized for hyper-parameter tuning using a grid search. All experiments, in this paper, were conducted using the INbreast dataset. The best obtained AUC from the experimental results is 0.994 +/- 0.003. Our results conclude that high-level distinctive features can be extracted from Mammograms by using the pre-trained DCNN, which can be used with the SVM classifier to robustly distinguish between the mass and non-mass presence in the breast.