南京邮电大学学报（自然科学版）2026，Vol.46Issue(3)：80-89,10.DOI:10.14132/j.cnki.1673-5439.2026.03.009
基于改进YOLOv8的眼底微动脉瘤检测算法
Fundus microaneurysm detection based on improved YOLOv8
摘要
Abstract
The detection of fundus microaneurysm holds important clinical significance for screening dia-betic retinopathy in early stage.Given the relatively small target area of microaneurysm lesions and the in-terference of other fundus structures during detection,a YOLO-FDS network model based on improved YOLOv8 is proposed.First,a lesion-centered region segmentation method is applied to the dataset for lo-cal enhancement.The C2f_Star module is introduced into the backbone network,which utilizes the star operation to be more lightweight while improving the feature fusion efficiency and performance.Second,the Neck part is improved into the FDPN-DASI network,enabling features at each scale to retain detailed contextual information.Then,the network can adaptively select the fused features to improve the saliency of the target.Finally,Wise-IoU is used as the loss function to effectively improve the overall performance of the detector.Experimental results demonstrate that the proposed algorithm achieves superior detection performance on the dataset,with an mAP of 84.6%,representing a 7.1%improvement over the original model.Additionally,the precision is increased by 6.06%and the recall rate by 6.95%,while the detec-tion speed is notably faster.关键词
眼底微动脉瘤/YOLOv8/医学图像检测/特征融合Key words
fundus microaneurysm/YOLOv8/medical image detection/feature fusion分类
信息技术与安全科学引用本文复制引用
吕辉,赵方暄..基于改进YOLOv8的眼底微动脉瘤检测算法[J].南京邮电大学学报（自然科学版）,2026,46(3):80-89,10.基金项目
河南省自然科学基金(242300420283)和河南省高校基本科研业务费专项(NSFRF240819)资助项目 （242300420283）
