基于YOLO目标检测模型的ABUS乳头定位系统
Localization system of nipple in ABUS based on YOLO detection model
陈嘉炜 1邱舜敏2
作者信息
- 1. 汕头大学,汕头 515063
- 2. 汕头大学医学院第一附属医院,汕头 515041
- 折叠
摘要
目的 乳腺癌的早期诊断至关重要.本文旨在利用深度学习模型对自动乳腺超声(Automatic Breast Ultrasound,ABUS)数据中的乳头区域进行精确检测,确保乳腺肿瘤在早期阶段能够获得可靠的技术诊断支持.方法 采用基于YOLO系列模型的方法,对ABUS冠状面图像中的乳头区域进行定位检测,为乳腺肿瘤的诊断提供位置基准.结果 在本任务中,YOLO系列模型均表现出色.特别是YOLOv5 模型,其精确率高达 0.955,召回率达到 0.925,帧率为 243,满足临床诊断需求.结论 YOLOv5 模型在ABUS乳头定位任务中性能优良,为乳腺肿瘤的早期发现提供了技术支持,具有重要的临床意义.
Abstract
Objective Early diagnosis of breast cancer is of paramount importance.We aim to utilize deep learning models to accurately detect the nipple area in Automated Breast Ultrasound(ABUS)data,ensuring reliable technical diagnostic support for breast tumors at an early stage.Methods Based on the YOLO series models,we locate and detect the nipple in ABUS coronal plane images,providing a positional reference for tumor diagnosis.Results The YOLO series models have all performed well.Particularly,the YOLOv5s model achieved a high precision rate of 0.955,a recall rate of 0.925,and a frame rate of 243,meeting the clinical diagnostic requirements.Conclusion The YOLOv5 model has demonstrated excellent performance in the ABUS nipple localization task.This technology provides crucial technical support for the early detection of breast tumors,with significant clinical implications.
关键词
自动乳腺超声/YOLO目标检测/乳头定位Key words
automated breast ultrasound/YOLO object detection/nipple location引用本文复制引用
出版年
2024
NETL
NSTL
万方数据