一种改进YOLOv5的CT图像肺结节检测方法

doi:10.11896/jsjkx.230500019

Abstract

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

CLC Number:

TP391

WU Chunming, LIU Yali. Method for Lung Nodule Detection on CT Images Using Improved YOLOv5[J].Computer Science, 2024, 51(6A): 230500019-6.

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Method for Lung Nodule Detection on CT Images Using Improved YOLOv5

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