[1]乐振,孙振,鞠瑞文,等.基于YOLOv8s的改进结核病病原体检测算法[J].中国医学物理学杂志,2024,41(7):910-917.[doi:DOI:10.3969/j.issn.1005-202X.2024.07.019]
　YUE Zhen,SUN Zhen,JU Ruiwen,et al.A novel tuberculosis pathogens detection algorithm based on YOLOv8s[J].Chinese Journal of Medical Physics,2024,41(7):910-917.[doi:DOI:10.3969/j.issn.1005-202X.2024.07.019]
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基于YOLOv8s的改进结核病病原体检测算法()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
41卷
期数:
2024年第7期
页码:
910-917
栏目:
医学人工智能
出版日期:
2024-07-25

文章信息/Info

Title:
A novel tuberculosis pathogens detection algorithm based on YOLOv8s
文章编号:
1005-202X（2024）07-0910-08
作者:
乐振1; 孙振2; 鞠瑞文2; 李庆党3
1.青岛科技大学信息科学技术学院， 山东 青岛 266061； 2.青岛科技大学数据科学学院， 山东 青岛 266061； 3.青岛科技大学中德科技学院， 山东 青岛 266061
Author(s):
YUE Zhen1;  SUN Zhen2;  JU Ruiwen2;  LI Qingdang3
1. School of Information Science and Technology, Qingdao University of Science and Technology, Qingdao 266061, China 2. School of Data Science, Qingdao University of Science and Technology, Qingdao 266061, China 3. College of Sino-German Science and Technology, Qingdao University of Science and Technology, Qingdao 266061, China
关键词:
结核病; 深度学习; YOLOv8; 注意力机制
Keywords:
Keywords: tuberculosis deep learning YOLOv8 attention mechanism
分类号:
R318；TP391
DOI:
DOI:10.3969/j.issn.1005-202X.2024.07.019
文献标志码:
A
摘要:
针对结核病病原体在痰涂片图像背景复杂且目标小，人工筛查成本较高的问题，提出了一种基于YOLOv8s的结核病病原体检测方法。首先，采用基于空间和通道重构卷积改进的结构来限制特征冗余。其次，引入了坐标注意力来扩大模型的感受野。再者，使用空间金字塔池化跨阶段局部网络来提取不同尺度上的特征信息。最后，嵌入基于归一化的注意力机制抑制不太显著的特征。实验结果表明，在公开数据集上，改进网络模型与原YOLOv8s模型相比，精确率和召回率分别提升2.7%和1.5%，置信度为0.5时的平均精度均值提高了2.3%，该模型能够有效辅助影像科医师进行诊断。
Abstract:
Abstract:To address the challenges of detecting tuberculosis pathogens in sputum smear images, such as complex backgrounds, small targets, and high costs of manual screening, a detection method based on YOLOv8s is presented. The structure is improved through spatial and channel reconstruction convolutions to limit feature redundancy, and a coordinate attention is introduced to expand the receptive field of the model. Furthermore, a spatial pyramid pooling cross-stage partial network is used to extract feature information at different scales, and a normalized attention mechanism is embedded to suppress less significant features. The experimental results on a public dataset show that compared with the original YOLOv8s model, the improved network model enhances precision and recall rates by 2.7% and 1.5%, respectively, and improved mean average precision at a confidence level of 0.5 by 2.3%, demonstrating that the improved model can effectively assist radiologists in diagnosis.

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备注/Memo

备注/Memo:
【收稿日期】2024-03-10 【基金项目】山东省泰山学者项目（tshw201502042） 【作者简介】乐振，硕士研究生，研究方向：医学图像处理，E-mail: yzhelloworld@163.com 【通信作者】李庆党，教授，研究方向：人工智能及大数据技术，E-mail: lqd@qust.edu.cn
更新日期/Last Update: 2024-07-13