Trauma condition identification and localization based on improved YOLOv5 algorithm
10.19745/j.1003-8868.2024165
- VernacularTitle:基于改进YOLOv5算法的创伤伤情识别与定位研究
- Author:
Yu-Shu WANG
1
;
Yong-Jian NIAN
;
Xue PENG
;
Jin XIE
;
Jun QI
;
Yao TAN
Author Information
1. 陆军军医大学第一附属医院急诊医学科,重庆 400038
- Keywords:
YOLOv5;
attention mechanism;
trauma condition;
trauma condition identification;
trauma condition localization
- From:
Chinese Medical Equipment Journal
2024;45(9):1-6
- CountryChina
- Language:Chinese
-
Abstract:
Objective To propose an attention mechanism-based YOLOv5 algorithm to relieve the wrong or missed diagnosis due to the complexity and variability of trauma conditions.Methods A YOLOv5-attention algorithm was constructed with YOLOv5 algorithm as the basic framework,which introduced the convolutional attention mechanism module into the feature fusion network and embedded the self-attention module at the end of the feature extraction network and the feature fusion network,respectively.The YOLOv5-attention algorithm was trained and validated on the Kaggle platform and compared with Fast-RCNN and YOLOv5 algorithms for determining fracture sites.Results The YOLOv5-attention algorithm achieved an average presicion of 0.859 8 for fracture site determination,which behaved better than Fast-RCNN algorithm with an average presicion of 0.697 5 and YOLOv5 algorithm with an average presicion of 0.847 1.Conclusion The YOLOv5-attention algorithm with high accuracy and robustness can identify and locate trauma conditions effectively and accurately.[Chinese Medical Equipment Journal,2024,45(9):1-6]
