Innovative Approaches in Diabetic Retinopathy Detection- A Comprehensive Study

The present study indicates that diabetic retinopathy (DR) is a common complication of diabetes, still causing blindness; therefore, screening procedures ought to be started immediately to avoid complications. Current methods of diagnosis pose a number of difficulties related to availability, expansion potential, and interpretation by a specialist. This systematic review focuses on the utilization of Machine and Deep learning methods for the diagnosis of diabetic retinopathy along with an extensive discussion on the latest advancements. In this study, several approaches are considered originating from the ML and, in particular – the DL theories: Levenberg–Marquardt (LM), CNNs, SVMs, and ensemble methods for the classification and grading of DR by using the retinal images. The study assesses the effectiveness of these models in increasing the diagnostic efficiency, decreasing the time consumption, and providing more efficient approaches in contrast to common practices. Essential and pressing issues that come along with AI, which include data quality, the requirement to develop big annotated datasets, and model explainability in healthcare settings are discussed. The study also provide case studies and application areas where the ML and DL models have been provided and their effectiveness demonstrated in helping the actual patients. From the objective of this study, the conclusions will depict the revolutionary feature of machine learning and deep learning in diagnosing retinopathy in diabetic patients. Thus, it is our intention to point out the existing drawbacks and stress the necessity of integrating a multifaceted team of professionals who will take collective responsibility for refining AI diagnostic tools for ophthalmological practice.