[1]王京华,袁金丽,郭志涛,等.改进的YOLOv4算法在肺结核检测中的应用研究[J].中国医学物理学杂志,2023,40(1):113-119.[doi:DOI:10.3969/j.issn.1005-202X.2023.01.019]
　WANG Jinghua,YUAN Jinli,GUO Zhitao,et al.Application of improved YOLOv4 algorithm in the detection of pulmonary tuberculosis[J].Chinese Journal of Medical Physics,2023,40(1):113-119.[doi:DOI:10.3969/j.issn.1005-202X.2023.01.019]
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改进的YOLOv4算法在肺结核检测中的应用研究()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
40卷
期数:
2023年第1期
页码:
113-119
栏目:
医学人工智能
出版日期:
2023-01-07

文章信息/Info

Title:
Application of improved YOLOv4 algorithm in the detection of pulmonary tuberculosis
文章编号:
1005-202X（2023）01-0113-07
作者:
王京华; 袁金丽; 郭志涛; 王佳浩
河北工业大学电子信息工程学院， 天津 300401
Author(s):
WANG Jinghua;  YUAN Jinli;  GUO Zhitao;  WANG Jiahao
School of Electronic and Information Engineering, Hebei University of Technology, Tianjin 300401, China
关键词:
肺结核; 深度学习; 特征融合; 坐标注意力; YOLOv4
Keywords:
Keywords: pulmonary tuberculosis deep learning feature fusion coordinate attention YOLOv4
分类号:
R318；TP391
DOI:
DOI:10.3969/j.issn.1005-202X.2023.01.019
文献标志码:
A
摘要:
针对CT影像中肺结核病灶复杂且尺度变化大造成检测精度低的问题，提出了一种改进特征融合方法的YOLOv4网络用于肺结核的检测。首先，采用尺度均衡的金字塔卷积来捕获不同尺度特征层之间的相互作用，并在此基础上以自适应空间特征融合的方式过滤掉不同尺度上的冲突信息，以进行特征的有效融合。其次，在低层特征上引入了坐标注意力以进一步提高小目标的检测精度。根据北京胸科医院提供的300例病患信息，搭建了一套规范的肺结核CT数据集，并在所构建的数据集上进行了实验。输入图片分辨率设定为512×512，与原始YOLOv4相比本文模型mAP提升了4.96%，且该指标优于现有主流肺结核检测算法，如Faster R_CNN、SSD、RetinaNet等。结果表明改进的YOLOv4算法能够有效解决检测目标尺度变化和小目标检测问题，提高检测精度。
Abstract:
Abstract: Aiming at the problem of low detection accuracy of pulmonary tuberculosis caused by the complex and large scale changes of tuberculosis lesions in CT images, YOLOv4 with an improved feature fusion block is proposed for the detection of pulmonary tuberculosis. Scale-equalizing pyramid convolution is used to capture the interaction between feature layers of different scales, and on this basis, the conflict information at different scales is filtered out by scale-equalizing adaptive spatial pyramid convolution, so as to achieve feature fusion effectively. In addition, coordinate attention is introduced on the low-level features for further improving the detection accuracy of small targets. A standardized tuberculosis CT data set is built using the information of 300 cases provided by Beijing Chest Hospital, and the experiments are conducted on the constructed data set. The input image resolution is set to 512×512. The results show that the proposed network increases mAP by 4.96% as compared with the original YOLOv4, and that it is better than the existing mainstream tuberculosis detection algorithms, such as Faster R_CNN, SSD, RetinaNet, etc. The improved YOLOv4 algorithm can effectively solve the problems of detection target scale changes and small target detection, thereby improving detection accuracy.

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备注/Memo

备注/Memo:
【收稿日期】2022-07-15 【基金项目】国家自然科学基金（61801164） 【作者简介】王京华，硕士研究生，研究方向：智能信息处理、计算机视觉、医学图像处理，E-mail: wjh_hebuter0676@163.com 【通信作者】袁金丽，博士，副教授，研究方向：机器学习、智能信息处理、医学图像处理，E-mail: 2005051@hebut.edu.cn
更新日期/Last Update: 2023-01-07