电子科技2026，Vol.39Issue(10)：63-70,8.DOI:10.16180/j.cnki.issn1007-7820.2026.10.007
基于混合卷积的糖尿病视网膜病变分类
Classification of Diabetic Retinopathy Based on Hybrid Convolution
摘要
Abstract
Diabetic retinopathy is one of the common complications for diabetic patients.If not diagnosed and treated in time,it may lead to blindness in patients.In order to capture image details more effectively and improve the feature extraction ability and classification accuracy,a diabetic retinopathy image classification network based on a hybrid convolution and attention mechanism is proposed.The proposed network is optimized based on the light-weight network DenseNet121,and the classification accuracy and feature extraction ability are improved by fusing the hybrid convolution and attention modules.The image is processed by the inception module,and multi-scale convolution is used to extract feature information of different scales,thereby enhancing the capture of details in reti-nal images.The extracted features are input into a dense block containing mixed convolution,optimizing the gradi-ent fluidity and feature transfer efficiency.The features are dynamically adjusted through the channel attention and spatial attention mechanisms to ensure the focus on the key lesion areas,and the extracted effective features are used for the classification of diabetic retinopathy images.The experimental results show that the classification accu-racy and sensitivity of the proposed method on the public dataset are superior to those of other models,which proves that it has potential advantages in practical applications and is conducive to the preliminary screening of clinical dia-betic retinopathy.关键词
糖尿病视网膜病变/混合卷积/注意力机制/图像分类/DenseNet121/轻量化网络Key words
diabetic retinopathy/hybrid convolution/attention mechanism/image classification/DenseNet121/lightweight network分类
信息技术与安全科学引用本文复制引用
张小宝,朱金荣,王颖,李朝越..基于混合卷积的糖尿病视网膜病变分类[J].电子科技,2026,39(10):63-70,8.基金项目
国家自然科学基金(62375234) （62375234）
江苏省研究生研究与实践创新计划(KYCX24_3714) National Natural Science Foundation of China(62375234) （KYCX24_3714）
Jiangsu Graduate Innovation Program(KYCX24_3714) （KYCX24_3714）
