Classification of diabetic retinopathy based on improved VOLO network
ZHAO Shuang
QIU Xiaoyu
KONG Xianglin
ZHANG Yaqi
Abstract：To improve the diagnostic efficiency of diabetic retinopathy(DR),we proposed a DR classification model based on the VOLO network.First,the depthwise convolution module was added to Outlook attention to improve the expression ability of attention.Then the normalization-based attention module was embedded in the backend of the attention mechanism to highlight the salient fea-tures,thereby completing the DR classification of lesion severity.The experimental results showed that the classification accuracy of the model reached 89.10％.This research model has good feasibility and can better assist doctors in clinical precision treatment.
Keywords：Fundus imagesImage classificationVision TransformerOutlookerDepthwise convolutionNormalization-based attention module
Publication Date：2024-08-28
Online Publishing Date：2026-09-11(First online date of this platform, not the publication date of the document)
Pages：8( 316-323 )