Paper Title
AI-Driven Retinal Image Analysis for Early Detection of Diabetic Retinopathy: Innovations and Future Prospects

Abstract
Early detection of Diabetic retinopathy proves crucial for maximizing the success of treatment because this condition stands as one of the leading blind-causing diseases. The research assesses how well four deep learning models (Convolutional Neural Networks (CNNs), ResNet, VGG16 and EfficientNet) perform at diagnosing diabetic retinopathy through retinal image analytics. The most accurate model proved to be EfficientNet because it reached 99.4% accuracy in classification results. Based on the analysis EfficientNet proves to be the best model for detecting diabetic retinopathy early. The research brings modern diagnostic methods through enhanced AI responses with better generalization abilities leading to better patient health results. Keywords - Diabetic Retinopathy, Deep Learning, CNN, ResNet, VGG16, EfficientNet, AI-Based Diagnosis, Retinal Image Analysis