计算机科学

计算机科学 ›› 2024, Vol. 51 ›› Issue (6A): 230500019-6.doi: 10.11896/jsjkx.230500019

• 图像处理&多媒体技术 • 上一篇    下一篇

一种改进YOLOv5的CT图像肺结节检测方法

邬春明, 刘亚丽   

  1. 东北电力大学电气工程学院 吉林 吉林 132000
  • 发布日期:2024-06-06
  • 通讯作者: 刘亚丽(452379093@qq.com)
  • 作者简介:(wuchunming@neepu.edu.cn)

Method for Lung Nodule Detection on CT Images Using Improved YOLOv5

WU Chunming, LIU Yali   

  1. College of Electrical Engineering,Northeast Electric Power University,Jilin,Jilin 132000,China
  • Published:2024-06-06
  • About author:WU Chunming,born in 1966,professor,master’s supervisor.His main research interests include image processing and wireless sensor networks.
    LIU Yali,born in 1996,postgraduate.Her main research interest is image processing.

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摘要： 针对YOLOv5算法对CT图像中的肺结节检测效果较差的问题,提出基于改进YOLOv5的肺结节检测方法。将YOLOv5网络中Neck部分的特征金字塔改进为加权双向特征金字塔网络;在YOLOv5网络中的Backbone部分加入高效通道注意力机制与坐标注意力机制。在LIDC-IDRI数据集上进行实验,结果表明,检测的平均精度可达80.2%,召回率可达90.75%,因此该方法能够有效检测肺结节。相较于YOLOv5算法,改进后的算法在mAP上提高了7.7%,在召回率上提高了5.5%。

关键词: 肺结节检测, 深度学习, 特征金字塔, 注意力机制

Abstract: To address the problem of poor detection results of lung nodules in CT images by YOLOv5 algorithm,an improved YOLOv5-based lung nodule detection method is proposed.The feature pyramid of the Neck part of the YOLOv5 network is improved to weighted bidirectional feature pyramid network.In the YOLOv5 network,the Backbone part adds an efficient channel attention mechanism and a coordinate attention mechanism.Experiments are conducted on the LIDC-IDRI dataset and the results show that the average detection accuracy id up to 80.2%,and the recall is up to 90.75%,so this method can effectively detect lung nodules.Compared with the YOLOv5 algorithm,the improved algorithm improves 7.7% in mAP and 5.5% in recall.

Key words: Pulmonary nodules detection, Deep learning, Feature pyramid, Attention mechanism

中图分类号: 

  • TP391
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