Revolutionizing Eye Disease Prediction in North-Eastern States of India: The Power of Deep Learning

Abstract

This study addresses the increasing prevalence of eye diseases, particularly those associated with diabetes, in the North-Eastern states of India. Focusing on conditions such as glaucoma, pterygium, dry eye, keratoconus, and keratitis, the research proposes the development of Deep Learning-based models. Employing advanced techniques like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), these models aim to capture nuanced patterns indicative of various eye conditions prevalent in the region. The systematic review critically assesses existing literature, with a specific focus on glaucoma prediction, and analyzes machine learning algorithms including KNN, RF, SVM, and NNET for accurate disease detection. Recognizing regional disparities in the distribution of ophthalmologists, particularly in underdeveloped regions like the North-Eastern states of India, the study explores the potential of Deep Learning-based screening as a cost-effective and efficient solution. Recent advancements in Deep Learning techniques, trained on color fundus images, show promise in automating the detection of various retinal diseases. The scope of existing models and their validation in a clinical setting are critically evaluated to establish their reliability and effectiveness in the specific demographic landscape of North-Eastern India. The study concludes with key insights into the current state of research, existing gaps, and the potential impact of the proposed Deep Learning-based model on the early detection and management of eye diseases in the North-Eastern region.