AN ENHANCED VGG16 BASED DEEP LEARNING FRAMEWORK FOR DIABETIC RETINOPATHY SEVERITY GRADING

Diabetic retinopathy (DR) is a common and severe effect of diabetes that contributes significantly to vision impairment worldwide. Retinal fundus imaging is widely used for the detection and monitoring of this condition; however, accurate interpretation of these images requires substantial clinical expertise. Hence, achieving consistent and reliable assessment is challenging, particularly given the limited availability of trained specialists. To address this issue, this work presents a revised VGG16 model for automatically classifying DR types. The proposed approach was assessed using standard evaluation metrics and achieved 95.43% accuracy on the preprocessed APTOS2019 fundus image dataset. The results suggest that the proposed method is well-suited for early screening and severity grading of diabetic retinopathy, enabling the timely diagnosis and lowering the risk of vision loss.