Systematic Review and Development of Advanced CNN Models for Enhanced Diagnostic Accuracy in Retinal Diseases

This study investigates the application of deep learning techniques, specifically Convolutional Neural Networks (CNNs), to detect and classify retinal disorders in fundus and Optical Coherence Tomography (OCT) images. It conducts a detailed analysis of current AI-based diagnostic approaches and demonstrates a revolutionary, multi-step deep learning methodology. The framework achieves exact classification and segmentation of retinal diseases by including fine-tuned pre-trained CNN architectures, with diagnostic accuracy comparable to or greater than that of human experts. Standard dataset evaluations have shown that it has the ability to considerably improve diagnostic processes in terms of speed, accuracy, and accessibility. Addressing the increased global prevalence of retinal illnesses and the dearth of qualified ophthalmologists, this study demonstrates how integrating complementary imaging modalities might improve AI-driven eye healthcare while laying a basis for future advances.