  Mammography is the most widely used gold standard for screening breast
cancer, where, mass detection is considered as the prominent step. Detecting
mass in the breast is, however, an arduous problem as they usually have large
variations between them in terms of shape, size, boundary, and texture. In this
literature, the process of mass detection is automated with the use of transfer
learning techniques of Deep Convolutional Neural Networks (DCNN). Pre-trained
VGG19 network is used to extract features which are then followed by bagged
decision 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 then
maximized during classifier selection and hyper-parameter tuning. The
robustness of the two selected type of classifiers, C-SVM, and \u{psion}-SVM,
are investigated with extensive experiments before selecting the best
performing classifier. All experiments in this paper were conducted using the
INbreast 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-trained
VGG19 network, high-level distinctive features can be extracted from Mammograms
which when used with the proposed SVM classifier is able to robustly
distinguish between the mass and non-mass present in breast.
