Improved YOLOv5 algorithm-based research on CT image recognition and segmentation for cerebral hemorrhage
10.19745/j.1003-8868.2025079
- VernacularTitle:基于改进YOLOv5算法的脑出血CT图像识别与分割研究
- Author:
Cheng-kun HONG
1
;
Tao YANG
;
Li-yuan FU
Author Information
1. 福建中医药大学福总教学医院(第九○○医院)放射诊断科,福州 350025;福建中医药大学第一临床医学院,福州 350122
- Publication Type:Journal Article
- Keywords:
YOLOv5 algorithm;
similarity attention mechanism;
cerebral haemorrhage;
CT image recognition;
CT image segmentation
- From:
Chinese Medical Equipment Journal
2025;46(5):1-8
- CountryChina
- Language:Chinese
-
Abstract:
Objective To modify the YOLOv5 algorithm with similarity attention mechanism(SimAM)to enhance the recognition and segmentation accuracy of CT images for cerebral hemorrhage.Methods A basic framework was established with a YOLOv5 algorithm consisting of a backbone network(Backbone),a neck module(Neck)and a head module(Head),and then SimAM was introduced at the end of Backbone to form a YOLOv5-Sim-B algorithm and at the end of Neck to construct a YOLOv5-Sim-N algorithm.The YOLOv5-Sim-B and YOLOv5-Sim-N algorithms were trained and validated using the CT image dataset for cerebral hemorrhage publicly available on the Kaggle competition platform,and compared with the traditional YOLOv5 algorithm for recognizing and segmenting cerebral hemorrhagic lesions in CT images.Results In case the value of IoU-T was 0.6,the mean average precision(mAP)was 0.967 for YOLOv5-Sim-B algorithm,0.960 for the YOLOv5-Sim-N algorithm and 0.964 for the traditional YOLOv5 algorithm during the recognition and segmentation of cerebral hemorrhagic lesions in CT images.Conclusion The proposed algorithm gains advantages in detection accuracy and robustness,and can efficiently identify and segment cerebral hemorrhage foci in CT images.[Chinese Medical Equipment Journal,2025,46(5):1-8]
