一种改进YOLOv5的CT图像肺结节检测方法
Method for Lung Nodule Detection on CT Images Using Improved YOLOv5
邬春明 1刘亚丽1
作者信息
- 1. 东北电力大学电气工程学院 吉林吉林 132000
- 折叠
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％.
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 im-proved 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.
关键词
肺结节检测/深度学习/特征金字塔/注意力机制Key words
Pulmonary nodules detection/Deep learning/Feature pyramid/Attention mechanism引用本文复制引用
出版年
2024
NETL
NSTL
万方数据