An Abnormity AI Detection Method of Breast Mammography

Abstract

We propose a method to detect the abnormity in mammograms by using a modified Faster-region convolution neural network (Faster R-CNN). In our approach, the Saliency detection was used to reserve the details of mass and improve the contrast between masses and normal tissues. The size of the anchor is modified to improve the mass detection accuracy. To better utilize the multi-level convolution features and enrich the discriminant information of each bounding box, the multi-level region-ofinterest pooling (MLRP) method substitutes for the original region-of-interest pooling method of Faster R-CNN. We demonstrate experimentally the good performance of the proposed method in abnormity detection of real clinical mammograms that relatively high detection accuracy is obtained with false positive rate reduced.