ABUS Nipple Object Detection Based on YOLOv5 Model
In recent years,the incidence rate of breast diseases,especially breast cancer,is on the rise,and its early diagnosis is particularly important.As a new three-dimensional ultrasound imaging technology,Automated Breast Ultrasound(ABUS)is helpful to improve the accuracy of breast cancer screening and diagnosis.In ABUS,the localization and segmentation of the nipple area is an important process.In order to solve the problem of difficulty in obtaining deep learning labels,this paper first proposes a semi-automatic label annotation method to simplify the process of obtaining data labels;Secondly,it is proposed to use the YOLOv5 object detection algorithm to detect nipple shadow areas in ABUS.Experimental test results show that the model can achieve an accuracy of 96.5%and a recall rate of 92.8%,and can reason with high efficiency,basically meeting the needs of engineering and clinical practice.