nkicsl/Fundus_Review
Official website of our paper: Applications of Deep Learning in Fundus Images: A Review. Newly-released datasets and recently-published papers will be updated regularly.
Comprehensive resource aggregating fundus image datasets, deep learning architectures, and benchmark results for retinal disease detection and classification tasks. Maintains curated experimental comparisons across multiple CNN and transformer-based approaches with their hyperparameter configurations, enabling reproducible evaluation on standard benchmarks like Messidor, EyePACS, and IDRiD. Serves as a living reference that extends the published survey with newly-released datasets and latest model performance metrics, linking directly to peer-reviewed implementations.
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