Abstract:
Diabetic Retinopathy (DR) is still among the leading causes of blindness in the working age
population worldwide. Manual screening is inconvenient and not feasible. Current research
proposes deployment of a light CNN, which was trained with the ODIR dataset (5,018
fundus images: 2,574 Normal and 2,444 Abnormal/DR), on a 70:15:15 train-validation-test
split. Dynamic class weights implemented within Binary Focal Loss handled imbalance-
induced bias. Compared to a DenseNet121 baseline of 83% accuracy implemented with Test
Time Augmentation, the proposed model achieved 98% accuracy and 0.98 (Normal) and
0.97 (Abnormal) F1-scores. Robustness was achieved with LAB color preprocessing and
CLAHE enhancement, real-time data augmentation, and stratified sampling. The model’s
efficiency enables Edge-AI deployment in low-resource environments. Future work will
incorporate Explainable AI and multi-source validation to enhance interpretability and
clinical reliability.
