Journal of Jishou University(Natural Sciences Edition) ›› 2023, Vol. 44 ›› Issue (1): 24-29.DOI: 10.13438/j.cnki.jdzk.2023.01.004

• Computer and Electrical Technology • Previous Articles     Next Articles

Improved YOLOv4 Lung Nodule Detection Algorithm

LIN Kunhuang,LI Jianfeng,WANG Yang,LIU Zhijie,LIU Zheyu   

  1. (College of Information Science and Engineering,Jishou University,Jishou 416000,Hunan China)
  • Online:2023-01-25 Published:2023-04-10

Abstract: An improved YOLOv4 lung nodule detection algorithm is designed to solve the problems of small target missing detection and lung nodule position distortion in the target detection YOLOv4 algorithm.On the basis of the original YOLOv4 network,the up sampling process of the feature fusion network is replaced by the bilinear interpolation method,and the tensor stacking method is used to make the semantic information of the top layer and the location information of the bottom layer to form a higher channel feature tensor.The experimental results show that,compared with the original YOLOv4 algorithm,the average accuracy and prediction speed of the improved YOLOv4 algorithm on the public dataset LUAN16 are improved by 4.54% and 28.1% respectively,and the visualization results have more accurate position expression.

Key words: lung nodules, target detection algorithm, YOLOv4, feature fusion

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