电子器件2025，Vol.48Issue(2)：372-379,8.DOI:10.3969/j.issn.1005-9490.2025.02.021
基于双注意力机制与多尺度融合的视网膜静脉阻塞检测
Detection of Retinal Vein Occlusion Based on Dual Attention Mechanism and Multi-Scale Fusion
摘要
Abstract
At present,the detection of retinal vein occlusion(RVO)almost depends on manual recognition,and it has problems such as low detection accuracy and slow detection speed.In response to this situation,on the basis of YOLOv5,a CSAE-YOLO network based on channel and spatial attention enhancement(CASE)is proposed.Firstly,the channel and spatial attention mechanisms are combined with E-ELAN to form a new CSAE-ELAN backbone network.CSAE-ELAN has efficient inter channel dependency and wider spatial attention of receptive field,which improves the ability to extract feature information.Secondly,the improved spatial pyramid pooling is used to en-hance the detection ability of lesions at different scales.The experimental results show that the mAP of the improved network is 87%,and the detection speed is 30 frame/s,which is far superior to other algorithms.It has the characteristics of high accuracy,small param-eter scale,and fast recognition speed.It provides a new AI aided diagnosis method for RVO detection.关键词
深度学习/医学图像检测/视网膜静脉阻塞检测/注意力机制/空间金字塔池化Key words
deep learning/medical image detection/detection of retinal vein occlusion/attention mechanism/SPP分类
信息技术与安全科学引用本文复制引用
吕辉,李沛洋,董帆,刘晓青..基于双注意力机制与多尺度融合的视网膜静脉阻塞检测[J].电子器件,2025,48(2):372-379,8.基金项目
河南省自然科学基金项目(242300420283),河南省高校基本科研业务费专项资金资助项目(NSFRF240819) （242300420283）
