# Enhancing DR Classification with Swin Transformer and Shifted Window Attention

arXiv: https://arxiv.org/abs/2504.15317 · HTML: https://mlsanity.com/paper/2504.15317/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 4 references that are in the map.
- First posted on arXiv 20 Apr 2025.

## Builds on (most cited first, 1 of 1)

- [Dual Branch Deep Learning Network for Detection and Stage Grading of Diabetic Retinopathy](https://mlsanity.com/paper/2308.09945/) (2023)

## Built on by (most cited first, 1 of 1)

- [From Pixels to Explanations: Interpretable Diabetic Retinopathy Grading with CNN-Transformer Ensembles, Visual Explainability and Vision-Language Models](https://mlsanity.com/paper/2604.23079/) (2026)

## Abstract (arXiv)

Diabetic retinopathy (DR) is a leading cause of blindness worldwide, underscoring the importance of early detection for effective treatment. However, automated DR classification remains challenging due to variations in image quality, class imbalance, and pixel-level similarities that hinder model training. To address these issues, we propose a robust preprocessing pipeline incorporating image cropping, Contrast-Limited Adaptive Histogram Equalization (CLAHE), and targeted data augmentation to improve model generalization and resilience. Our approach leverages the Swin Transformer, which utilizes hierarchical token processing and shifted window attention to efficiently capture fine-grained features while maintaining linear computational complexity. We validate our method on the Aptos and IDRiD datasets for multi-class DR classification, achieving accuracy rates of 89.65% and 97.40%, respectively. These results demonstrate the effectiveness of our model, particularly in detecting early-stage DR, highlighting its potential for improving automated retinal screening in clinical settings.