Deep Learning Paradigms for Enhanced Diabetic Retinopathy Detection: An In-Depth Analysis of CNN-Based Techniques and Clinical Utility

Recently, advances in diagnostic systems using computer have shown promise of increasing DR detection from color fundus images with the help of deep learning methods especially on convolutional neural networks (CNNs). This study investigates the effectiveness of CNNs for medical image analysis by considering the most advanced deep learning-based techniques available today. In order to detect and classify DR. In addition, data sets for DR color fundus retinal imaging are examined. Sindh Institute of Ophthalmology and Visual Sciences (SIOVS) conducted an in- depth study including training, validation and testing of the model using proprietary data sets. Clinicians' experts agree that there is high accuracy in results: 93.72%. The research reveals how much potential lies in using deep learning models to aid clinical decisions in ophthalmology, thus stressing the importance of artificial intelligence as a tool for diagnosing early stages of diabetic retinopathy. The likelihood of vision loss in diabetic patients can be reduced by addressing the urgent need for effective and accurate methods for DR screening.