WE‐DE‐207B‐02: Detection of Masses On Mammograms Using Deep Convolutional Neural Network: A Feasibility Study

Abstract

Purpose:To develop a deep convolutional neural network (DCNN)‐based computer‐aided diagnosis (CAD) system for detecting the masses in digital mammographic images.Methods:A DCNN architecture, which consists of 5 convolutional layers and 3 fully connected layers, is constructed in this study. The DCNN parameters are then trained by the following two procedures. We first train the DCNN using about 1.3 million natural images for classification of 1,000 categories. Then, we modify the last fully connected layer and subsequently train the modified DCNN using 1,656 mammographic region of interest (ROI) images for two categories classification: mass and normal.Results:The trained DCNN is tested by using 198 mammographic ROI images including 99 mass images and 99 normal images. The experimental results show that the sensitivity of the mass detection is about 89.9% and the false positive is 19.2%. These results demonstrated that the DCNN has a potential for mammographic CAD.Conclusion:In recent years, the DCNN, as one of the most successful techniques in deep learning technology, made a remarkable impact on image recognition application. For medical image recognition, however, its performance is uncertainty because collecting a large amount of training image data for a particularly medical image modality is difficult. In this study, our preliminary experiments demonstrated a feasibility to apply the DCNN in mammographic CAD system. To the best of our knowledge, this study is also the first demonstration of DCNN for detecting the masses in mammographic images.