Combining deep convolutional networks and SVMs for mass detection on digital mammograms

It is important to detect breast cancers as early as possible, which are commonly diagnosed as a mass region on mammograms. Deep Convolutional networks (ConvNets) have been specially designed for various computer vision tasks. In image classification, it contains many layers to automatically extract image features and employs the softmax function at the last layer to predict a probability. Although it excels in feature extraction, the classification is still limited. In this paper, we propose to apply SVMs into ConvNets to detect a mass on mammograms. To overcome the scarcity of training images, a data augmentation technique is employed to increase the sample data. To further enhance the accuracy, two recent techniques in ConvNets are applied including (i) rectified linear units and (ii) dropout. The experiment was conducted on the INbreast data set. The result showed that the proposed method achieved an accuracy at 98.44%, which is superior to the baseline (ConvNets) for 8%.