Enhancing Diabetic Retinopathy Grading with Advanced Diffusion Models

Recently, there has been a substantial surge in interest surrounding diffusion models, which are considered a prominent class of generative models. This surge is primarily attributed to their potential applications in a variety of deep learning problems. The primary objective of this study is to assess the effectiveness of diffusion models as a data augmentation technique in the context of medical image analysis. Furthermore, it aims to conduct a comparative analysis of the performance exhibited by deep learning classifiers trained on two distinct datasets. One dataset is augmented using the diffusion model, while the other dataset undergoes traditional data augmentation techniques. Utilizing the IDRiD dataset for the purpose of diabetic retinopathy diagnosis, the results demonstrate the efficiency of the diffusion model as a data augmentation technique for medical images compared to traditional data augmentation techniques. The integration of diffusion model augmented data yields superior performance for both classifiers. Namely, the fine-tuned ResNet-50 reached an accuracy of 53.40%, and the proposed CNN-based approach reached an accuracy of 44.66%, surpassing the performance of classifiers trained using traditional data augmentation techniques.