Research of Breast Molybdenum Target Mass Detection Algorithm Based on Cascade R-CNN
Breast cancer has complex biological characteristics and high malignancy,ranking the first place in the incidence rate of female malignant tumors.X ray examination of mammographic mass is an important way to diagnose breast cancer early.How-ever,the detection of breast molybdenum target mass is still in the early stage,and the existing computer-aided detection accuracy is low.To solve this problem,a breast molybdenum target mass detection method based on Cascade R-CNN is proposed in this pa-per.Using the breast X-ray data set of the University of South Florida,breast molybdenum target masses are divided into benign and malignant.By adding the attention module to the feature network,rich features of breast molybdenum target masses are extract-ed.In addition,this paper proposes a new FPN network FA-FPN,which further improves the extraction of lesion features of breast molybdenum target masses,and solves the problem that the biological characteristics of breast molybdenum target masses are com-plex and difficult to extract features.The experimental results show that the map value of the model on the breast X-ray data set of the University of South Florida reaches 82.9%,especially under AP75.This method has good performance in the detection of breast molybdenum target mass,can improve the detection accuracy of breast molybdenum target mass,and avoid false detection and missed detection to a certain extent.
mammography mass detectionCascade R-CNNfeature extractionFPN
国家科技期刊平台
NSTL
万方数据
