A Deep Learning Approach for Diabetic Retinopathy Classification Using Retinal Images

Diabetic Retinopathy (DR) is a major cause of visual impairment worldwide, especially in diabetic patients. Early detection is extremely important for effective management and the prevention of serious complications. The deep learning approach for classifying diabetic retinopathy using retinal imaging is an automated system for classifying diabetic retinopathy using deep learning techniques. The folding neural network (CNN) model architecture analyzes retinal images and classifies them with high accuracy as diabetic or non-diabetic classification. The proposed framework includes extensive prepossessing steps such as image size, normalization, and data expansion to improve model robustness. A custom CNN model has been developed. This included a folding layer with batch normalization and dropouts to improve characteristic extraction, simultaneously reducing overhang. This model was trained on a diverse dataset of retinal images and validated using standard metrics including accuracy, accuracy, recall, F1 scores, and confusion matrix. The results show that the system achieves a high level of classification accuracy. This demonstrates its potential as a reliable tool for screening for early diabetic retinopathy. Performance analysis and training history visualization continue to validate the validity and generalizability of the model. It will help contribute in the field of medical imaging by providing an accessible, AI-controlled solution for preventive diabetic health care and highlighting the importance of early diagnosis to improve patient outcomes.