测试技术学报2025，Vol.39Issue(2)：130-137,8.DOI:10.62756/csjs.1671-7449.2025029
基于改进YOLOv5的视网膜黄斑病变分类检测算法
Classification and Detection Algorithm of Macular Disease Based on Improved YOLOv5
摘要
Abstract
Macular disease is one of the important causes of blindness,manual screening is prone to misdi-agnosis,and automatic diagnosis based on deep learning helps early detection and treatment.A classifica-tion and detection algorithm for macular lesions based on improved YOLOv5 was proposed.To solve the problem of insufficient fusion of fine features in macular lesions images,a weighted bidirectional feature pyramid network replaced the PANet feature fusion module of YOLOv5 neck to achieve efficient multi-scale feature fusion to obtain better-detailed features of macular lesions.To solve the problem of poor detection ability of small target lesions,the SK attention mechanism was introduced into the model to enhance the capture of regional features of macular lesions by adjusting the receptive field adaptively.Com-parative experiments show that the proposed algorithm can improve the detection accuracy of small targets from 91.9%to 94.2%,and the average accuracy of the whole class from 93.4%to 96.6%.Moreover,under the same conditions,the algorithm performs better than other target detection network models.关键词
目标检测/视网膜黄斑病变/加权双向特征金字塔网络/注意力机制Key words
object detection/macular degeneration of the retina/weighted bidirectional feature pyramid network/attention mechanism分类
计算机与自动化引用本文复制引用
王楠楠,吴其洲,王召巴,金永..基于改进YOLOv5的视网膜黄斑病变分类检测算法[J].测试技术学报,2025,39(2):130-137,8.基金项目
山西省自然科学基金资助项目(202103021224202) （202103021224202）
山西省归国留学人员科研基金资助项目(20210038) （20210038）
