ArticleLasers in medical science2024
Automated classification of choroidal neovascularization, diabetic macular edema, and drusen from retinal OCT images using vision transformers: a comparative study.
Article in Lasers in medical science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Noise-aware dynamic convolution for improved generalizability of retinal disease diagnosis using optical coherence tomography images.Journal of biomedical optics · 2026Article
- Federated Learning for Multi-Disease Ophthalmic Diagnostics Using OCT Angiography.Ophthalmology science · 2026Article
- Artificial intelligence-driven diabetic retinopathy research: mapping the evolution, coupling, and global collaboration landscape (1996-2026).Frontiers in endocrinology · 2026Review
- Evaluation of classification performance for six types of fundus diseases in OCT images based on multi-source training strategy.Frontiers in medicine · 2026Article
- A two-stage deep learning framework with progressive feature fusion and attention mechanisms for multi-class retinal disease classification.Frontiers in artificial intelligence · 2026Article
- Compact vision language models enable efficient and interpretable optical coherence tomography through layer-specific multimodal learning.Communications medicine · 2025Article
- Artificial intelligence-driven diagnosis for age-related macular degeneration bridging pathology and engineering: a survey.International ophthalmology · 2025Review
- Multi-stage framework using transformer models, feature fusion and ensemble learning for enhancing eye disease classification.Scientific reports · 2025Article
- Residual self-attention vision transformer for detecting acquired vitelliform lesions and age-related macular drusen.Scientific reports · 2025Article
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Authors and funding
4 authors.
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Abstract
Classifying retinal diseases is a complex problem because the early problematic areas of retinal disorders are quite small and conservative. In recent years, Transformer architectures have been successfully applied to solve various retinal related health problems. Age-related macular degeneration (AMD) and diabetic macular edema (DME), two prevalent retinal diseases, can cause partial or total blindness. Diseases therefore require an early and accurate detection. In this study, we proposed Vision Transformer (ViT), Tokens-To-Token Vision Transformer (T2T-ViT) and Mobile Vision Transformer (Mobile-ViT) algorithms to detect choroidal neovascularization (CNV), drusen, and diabetic macular edema (DME), and normal using optical coherence tomography (OCT) images. The predictive accuracies of ViT, T2T-ViT and Mobile-ViT achieved on the dataset for the classification of OCT images are 95.14%, 96.07% and 99.17% respectively. Experimental results obtained from ViT approaches showed that Mobile-ViT have superior performance with regard to classification accuracy in comparison with the others. Overall, it has been observed that ViT architectures have the capacity to classify with high accuracy in the diagnosis of retinal diseases.
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