为解决现有的基于深度学习的结直肠息肉检测算法计算复杂或检测精度较低,不能在速度和精度方面同时满足实时检测的问题,提出了一种基于单阶段目标检测网络YOLO(you only look once)v5s的结直肠腺瘤实时检测方法.在YOLOv5s的主干网中融入通道注意力机制,并以BCEWithLogitsLoss代替其原有的交叉熵损失函数BCELogits,以此提升网络性能.选取2 074张腺瘤图片和包含19 700帧的20段腺瘤视频,按照3∶1的比例构建结直肠腺瘤训练集和测试集.测试结果表明,结直肠腺瘤检测的平均精度为93.6%,检测速度为93帧/s,验证了该系统可以在肠镜的退镜过程中实时检测腺瘤性息肉,且具有较好的检测性能.
There are two major issues in the existing deep learning-based algorithms for colorectal polyp detection which are leading to the failure of real-time detection: one is the complicate calculation and the other is poor detection precision. In this paper, we proposed a real-time method for colorectal adenoma detection based on a single-stage target detection network YOLOv5s, in which the channel attention mechanism was added to the backbone and the loss function of BCEWithLogitsLoss was adopted instead of the original one of BCELogits. We collected 2074 endoscopic adenoma images and 20 adenoma videos with 19700 frames, and then assigned them as training dataset and testing dataset according to the ratio of 3∶1. - DOI:10.3969/j.issn.1000-1565.2022.03.016一种基于改进YOLOv5s网络的结直肠腺瘤实时检测方法刘爽1,田兆星1,李浩然1,常颖2,吴思蓓1,薛林雁1(1.河北大学 质量技术监督学院,计量仪器与系统国家地方联合工程研究中心,河北 保定 071002;2.河北大学附属医院 消化内科,河北 保定 071000)摘 要:为解决现有的基于深度学习的结直肠息肉检测算法计算复杂或检测精度较低,不能在速度和精度方面同时满足实时检测的问题,提出了一种基于单阶段目标检测网络YOLO(you only look once)v5s的结直肠腺瘤实时检测方法.在YOLOv5s的主干网中融入通道注意力机制,并以BCEWithLogitsLoss代替其原有的交叉熵损失函数BCELogits,以此提升网络性能.选取2 074张腺瘤图片和包含19 700帧的20段腺瘤视频,按照3∶1的比例构建结直肠腺瘤训练集和测试集.测试结果表明,结直肠腺瘤检测的平均精度为93.6%,检测速度为93帧/s,验证了该系统可以在肠镜的退镜过程中实时检测腺瘤性息肉,且具有较好的检测性能.关键词:计算机辅助诊断;腺瘤;卷积神经网络;YOLOv5s;实时检测中图分类号:TP391.7 文献标志码:A 文章编号:1000-1565(2022)03-0327-10A real-time method for colorectal adenoma detection based on an improved YOLOv5s networkLIU Shuang1, TIAN Zhaoxing1, LI Haoran1, CHANG Ying2, WU Sibei1, XUE Linyan1(1. National & Local Joint Engineering Research Center of Metrology Instrument and System, College of Quality and Technical Supervision, Hebei University, Baoding 071002, China; 2. Department of Gastroenterology, Affiliated Hospital of Hebei University, Baoding 071000, China)Abstract: There are two major issues in the existing deep learning-based algorithms for colorectal polyp detection which are leading to the failure of real-time detection: one is the complicate calculation and the other is poor detection precision. In this paper, we proposed a real-time method for colorectal adenoma detection based on a single-stage target detection network YOLOv5s, in which the channel attention mechanism was added to the backbone and the loss function of BCEWithLogitsLoss was adopted instead of the original one of BCELogits. We collected 2074 endoscopic adenoma images and 20 adenoma videos with 19700 frames, and then assigned them as training dataset and testing dataset according to the ratio of 3∶1. - 收稿日期:2021-10-14 基金项目:河北省自然科学基金资助项目(H2019201378);河北大学自然科学多学科交叉研究计划资助项目(DXK201914);河北大学校长科研基金资助项目(XZJJ201914;XZJJ201917;XZJJ201918);大中学生科技创新能力培育专项项目(22E50041D) 第一作者:刘爽(1981—),女,河北保定人,河北大学副教授,博士,主要从事生物医学图像处理方向研究.E-mail:lianlianfushi@126.com 通信作者:薛林雁(1981—),女,河北广平人,河北大学副教授,博士,主要从事生物医学图像处理、计算机视觉方向研究.E-mail:lineysnow@163.com第3期刘爽等:一种基于改进YOLOv5s网络的结直肠腺瘤实时检测方法The experimental results show that the improved network achieved a mean average precision of 93.6% and a detection speed of 93FPS, which implies that the system can detect adenomatous polyps in real time during the retraction of colonoscopy, and outperforms the original state-of-the-art method.
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