基于YOLOv3的乳腺X线图像肿块检测方法
YOLOV3-BASED BREAST MASS DETECTION METHOD IN MAMMOGRAPHY
潘以轩 1陈智丽 1高皓 1张辉 1夏兴华1
作者信息
- 1. 沈阳建筑大学信息与控制工程学院 辽宁沈阳 110168
- 折叠
摘要
乳腺X线摄影术是目前国际上公认的有效的乳腺癌早期筛查手段.提出一种基于YOLOv3网络的乳腺X线图像肿块检测方法.该方法能够在保证精度的同时,以较快的速度一次完成对整幅图像中肿块的检测.应用迁移学习技术,将由数字化乳腺X线图像学习到的肿块病变检测知识迁移到全域数字图像,有效解决了目前全域数字图像数据集缺乏的问题.使用五折交叉验证方法,在DDSM和INbreast数据集上进行实验验证,最终得到的五折间肿块检测平均准确率为81.34％.
Abstract
Mammography is internationally recognized as an effective screening tool for early breast cancer.This paper proposes a mammographic mass detection method based on YOLOv3 network.The method could complete mass detection of the whole image at a faster speed while ensuring accuracy.By applying transfer learning technology,the mass lesion detection knowledge learned from the digitized mammograms were transferred to the full-field digital mammograms,which effectively solved the current lack of full-field digital mammography datasets.The five-fold cross-validation method was used for evaluation based on DDSM and INbreast datasets.Through extensive experiments,the obtained average accuracy of the mass detection over the five folds is 81.34％.
关键词
深度学习/YOLOv3/乳腺X线图像/肿块检测/迁移学习Key words
Deep learning/YOLOv3/Mammography/Mass detection/Transfer learning引用本文复制引用
基金项目
国家自然科学基金项目(61602322)
辽宁省自然科学基金项目(20180550059)
辽宁省教育厅重点攻关项目(lnzd201904)
出版年
2024
国家科技期刊平台
NSTL
万方数据