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Mammography is the most widely used gold standard for screening breastcancer, where, mass detection is considered as the prominent step. Detectingmass in the breast is, however, an arduous problem as they usually have largevariations between them in terms of shape, size, boundary, and texture. In thisliterature, the process of mass detection is automated with the use of transferlearning techniques of Deep Convolutional Neural Networks (DCNN). Pre-trainedVGG19 network is used to extract features which are then followed by baggeddecision tree for features selection and then a Support Vector Machine (SVM)classifier is trained and used for classifying between the mass and non-mass.Area Under ROC Curve (AUC) is chosen as the performance metric, which is thenmaximized during classifier selection and hyper-parameter tuning. Therobustness of the two selected type of classifiers, C-SVM, and \u{psion}-SVM,are investigated with extensive experiments before selecting the bestperforming classifier. All experiments in this paper were conducted using theINbreast dataset. The best AUC obtained from the experimental results is 0.994+/- 0.003 i.e. [0.991, 0.997]. Our results conclude that by using pre-trainedVGG19 network, high-level distinctive features can be extracted from Mammogramswhich when used with the proposed SVM classifier is able to robustlydistinguish between the mass and non-mass present in breast.

Automatic Mass Detection in Breast Using Deep Convolutional Neural Network and SVM Classifier