CAD system uses deep learning to detect, segment, classify masses from mammograms

Researchers have developed a fully integrated computer-aided diagnosis (CAD) system that detects, segments and classifies masses from mammograms using deep learning and a deep convolutional neural network (CNN), according to a new study published by the International Journal of Medical Informatics. The authors used a proven regional deep learning model, You-Only-Look-Once (YOLO), for detecting potential masses in the breast tissues of patient data taken from a publicly available dataset. They provided a number of reasons in their analysis. "First, YOLO has a robust ability to detect the masses directly from entire mammograms," wrote author Mugahed A. Al-antari, PhD, of Kyung Hee University in Yongin, South Korea, and colleagues. "Second, detected bounding boxes via YOLO accurately align the masses, thereby, a low rate of false positives is achieved compared with other studies. Third, it can even detect challenging cases where the masses exist either over pectoral muscles or inside dense regions. Fourth, the running time of the testing and required memory are extremely low compared to other more complex deep learning models."