ArticleJournal of healthcare engineering2022
Attentional Mechanisms and Improved Residual Networks for Diabetic Retinopathy Severity Classification.
Article in Journal of healthcare engineering, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- A Self-Supervised Equivariant Refinement Classification Network for Diabetic Retinopathy Classification.Journal of imaging informatics in medicine · 2025Article
- A novel fusion of genetic grey wolf optimization and kernel extreme learning machines for precise diabetic eye disease classification.PloS one · 2024Article
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Authors and funding
5 authors.
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Abstract
Diabetic retinopathy is a main cause of blindness in diabetic patients; therefore, detection and treatment of diabetic retinopathy at an early stage has an important effect on delaying and avoiding vision loss. In this paper, we propose a feasible solution for diabetic retinopathy classification using ResNet as the backbone network. By modifying the structure of the residual blocks and improving the downsampling level, we can increase the feature information of the hidden layer feature maps. In addition, attention mechanism is utilized to enhance the feature extraction effect. The experimental results show that the proposed model can effectively detect and classify diabetic retinopathy and achieve better results than the original model.
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