Final Year Project (FYP)Authors: Tran, Van Duat; Advisor: Pham, Thi Viet Huong (2025)
Diabetic retinopathy (DR) is a leading cause of vision impairment globally. Early and accurate detection of DR severity is crucial for timely intervention and prevention of blindness. This study investigates the potential of two cutting-edge deep learning models, Swin Transformer and FastViT, for automated DR severity classification from fundus images. To enhance the visibility of pathological features crucial for accurate classification, a novel image preprocessing pipeline is proposed. This pipeline combines Contrast Limited Adaptive Histogram Equalization (CLAHE) with Top-hat and Blackhat morphological operations, aiming to improve contrast and effectively highlight salient feature... 
