Improved YOLOv8 algorithm-based detection of pulmonary nodules in CT images
10.19745/j.1003-8868.2025136
- VernacularTitle:基于改进YOLOv8算法的CT图像肺结节检测研究
- Author:
Cheng-kun HONG
1
;
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:
YOLOv8 algorithm;
CT image;
attention mechanism;
detection of pulmonary nodule;
pulmonary module
- From:
Chinese Medical Equipment Journal
2025;46(8):1-10
- CountryChina
- Language:Chinese
-
Abstract:
Objective To propose an improved YOLOv8 algorithm based on polarized self-attention(PSA)and deformable attention(DAT)so as to enhance the detection of pulmonary nodules in CT images.Methods A basic framework was established with a YOLOv8 model consisting of a backbone network(Backbone),a neck module(Neck)and a head module(Head).PSA was introduced into the end of the spatial pyramid pooling-fast(SPPF)of Backbone to construct a YOLOv8-PSA algorithm,and DAT was involved in the medium-scale feature layer P4 in Head to form a YOLOv8-DAT algorithm.The YOLOv8-PSA and YOLOv8-DAT algorithms were trained and validated using the CT image dataset of pulmonary nodules from public platforms,and compared with the original YOLOv8 algorithm for the detection of pulmonary nodule lesions in CT images.Results When used for pulmonary module detection of CT images,the YOLOv8-DAT algorithm had the mean average precision(mAP)in case of intersection over union threshold of 0.5(mAP50),mAP in case of intersection over union threshold of 0.5 to 0.95(mAP50-95)and precision ratio being 0.918,0.588 and 0.960 respectively,which gained advantages over the YOLOv8-PSA algorithm with mAP50,mAP50-95 and precision ratio being 0.914,0.583 and 0.945 respectively,and over the original YOLOv8 algorithm with mAP50,mAP50-95 and precision ratio being 0.911,0.564 and 0.952 respectively.Conclusion The YOLOv8-DAT algorithm detects pulmonary modules in CT images effectively,and facilitates early screening and diagnosis of pulmonary modules clinically.[Chinese Medical Equipment Journal,2025,46(8):1-10]
