CAD-ViT: Coordinate Attention-Enhanced Vision Transformer With Dilated Feature Fusion for Diabetic Retinopathy Staging

Automatic Diabetic Retinopathy classification is crucial as it aids in determining the disease stage, enabling timely treatment to prevent progression to low vision and significantly reducing the diagnostic burden on ophthalmologists. Efficient and accurate network models are essential for clinical applications. In this paper, we propose a deep learning method for DR diagnosis and grading based on multi-scale feature fusion and attention guidance. This approach employs dilated convolutions with varying dilation rates to expand the receptive field and integrate features at multiple scales. Additionally, an attention mechanism based on spatial directional feature aggregation is embedded in the inverted residual modules to guide the model to focus on critical lesion features in fundus images. Furthermore, transformers are utilized to extract global features, enhancing model performance. Experiments conducted on the APTOS-2019 and DDR datasets demonstrate that our method achieves a diagnostic accuracy of 99.2% and grading accuracy of 88.6% on the APTOS-2019 dataset, and a grading accuracy of 84.8% on the DDR dataset. Experimental results show that the proposed method performs excellently and outperforms many recently proposed popular DR diagnosis and classification methods.