Diabetic Retinopathy Classification Using Swin Transformer with Multi Wavelet

Abstract

Diabetic retinopathy (DR) impacts over a third of individuals diagnosed with diabetes and stands as the leading cause of vision loss in working-age adults worldwide. Therefore, the early detection and treatment of DR can play a crucial role in minimizing vision loss. This research paper proposes a novel technique that combines Wavelet and multi-Wavelet transforms with Swin Transformer to automatically identify the progression level of diabetic retinopathy. A notable innovation of this study lies in the implementation of the multi-Wavelet transform for extracting relevant features. By incorporating the resulting images into the Swin Transformer model, a unique approach is introduced during the feature extraction phase. The researchers conducted experiments using the publicly available Kaggle APTOS 2019 dataset, which comprises 3662 images. The achieved training accuracy in the experiments was an impressive 97.78%, with a test accuracy of 97.54%. The highest accuracy observed during training reached 98.09%. In comparison, when applying the multi-Wavelet approach to multiclass classification, the training and validation accuracies were 91.60% and 82.42%, respectively, with a testing accuracy of 82%. These results indicate that the multi-Wavelet approach outperforms alternative methods in the study. The model demonstrated exceptional performance in binary classification tasks, exhibiting high accuracies on both the training and test sets. However, it is important to note that the model's accuracy decreased when employed in multiclass classification, emphasizing the need for further investigation and refinement to handle more diverse classification scenarios.