Expert Systems based Diagnostics of Diabetic Retinopathy

Abstract

Diabetic Retinopathy (DR) is a prevalent and critical complication of diabetes, often resulting in vision impairment and blindness if not promptly diagnosed and managed. This research endeavours to enhance the accuracy and efficiency of DR diagnosis by harnessing the power of machine learning (ML) models. Leveraging a comprehensive dataset containing retinal images annotated with diverse DR stages, these models have been meticulously trained and rigorously validated. Employing state-of-the-art ML algorithms, particularly convolutional neural networks, our models exhibit an exceptional ability to detect patterns and anomalies indicative of DR. The results demonstrate that ML models offer a promising and competitive alternative to conventional diagnostic practices. By potentially revolutionizing DR diagnostic procedures, these innovative ML approaches have the capacity to facilitate early detection and intervention, ultimately improving patient outcomes and alleviating the healthcare system's burden.