A Deep Learning Approach for Breast Cancer Mass Detection

Abstract

Breast cancer is the most widespread type of cancer among women. The diagnosis of breast cancer in its early stages is still a significant problem worldwide. The accurate classification and localization of breast mass help in the early detection of the disease, so in the last few years, a variety of CAD systems are developed to enhance breast cancer classification and localization accuracy, but most of them are fully based on handcrafted feature extraction techniques, which affect its efficiency. Currently, deep learning approaches are able to automatically learn a set of high-level features and consequently, they are achieving remarkable results in object classification and detection tasks. In this paper, the pre-trained ResNet-50 architecture and the Class Activation Map (CAM) technique are employed in breast cancer classification and localization respectively. CAM technique exploits the Convolutional Neural Network (CNN) classifiers with Global Average Pooling (GAP) layer for object localization without any supervised information about its location. According to the experimental results, the proposed approach achieved 96% Area under the Receiver Operating Characteristics (ROC) curve in the classification with 99.8% sensitivity and 82.1% specificity. Furthermore, it is able to localize 93.67% of the masses at an average of 0.122 false positives per image on the Digital Database for Screening Mammography (DDSM) data-set. It is worth noting that the pre-trained CNN is able automatically to learn the most discriminative features in the mammogram, and then fulfills superior results in breast cancer classification (normal or mass). Additionally, CAM exhibits the concrete relation between the mass located in the mammogram and the discriminative features learned by the CNN.