Sharper Detection: Enhanced Techniques for Diabetic Retinopathy Grading

Diabetic retinopathy (DR) is a leading cause of blindness, and deep learning methods are extensively used for automated DR grading. This paper demonstrates how applying techniques such as oversampling, data augmentation, K-fold cross-validation, learning rate scheduling, and early stopping can significantly improve performance for models such as ResNet18 and ResNet50. Using the APTOS and EyePACS datasets, our results reveal a substantial accuracy improvement compared to prior methods, which rely on oversampling and data augmentation as baseline techniques. We obtained an accuracy of 97.78% on APTOS with ResNet18 and 93.80% on EyePACS with ResNet50, outperforming the baseline. The techniques presented offer a valuable framework that can complement and enhance the state-of-the-art methods for DR grading and beyond.