AI-Driven Automated Screening of Diabetic Retinopathy: A Comprehensive Review of Convolutional Neural Networks for Early Detection

Diabetic Retinopathy (DR) is a common complication of diabetes and remains one of the leading causes of vision loss worldwide, particularly among the working-age population. Because early stages often present no noticeable symptoms, timely and regular screening is essential to prevent permanent damage and long-term visual disability. Manual examination of retinal fundus images requires trained ophthalmologists, specialized equipment, and considerable effort, which limits the feasibility of large-scale screening programs, especially in resource-constrained settings. Recent advances in deep learning, particularly Convolutional Neural Networks (CNNs), have demonstrated strong potential in automated medical image analysis and decision support systems. This paper reviews recent developments in CNN-based approaches for the detection and grading of DR. Image preprocessing methods, data augmentation strategies, widely used network architectures, and transfer learning techniques are discussed in detail. Key challenges such as class imbalance, interpretability, and cross-dataset variability are also examined. Finally, future directions focusing on efficient, scalable, and deployable screening systems are outlined to support real-world clinical integration.