Authors : Shaikh Nazim Gufran1, Dr. Dipak R Nemade
DOI : 10.46335/IJIES.2025.10.6.15
Abstract –Eye diseases are a leading cause of visual impairments and blindness around the world. To prevent vision loss due to these diseases and improve patients' quality of life, early detection methods are essential for timely treatment. At the moment, skilled ophthalmologists perform manual examinations in order to diagnose eye conditions. But in recent years, deep learning methods for categorizing eye conditions have showed a lot of promise. A comprehensive analysis of machine learning models for categorizing different eye conditions is presented in this study. We examine these models from four perspectives: problem nature and challenges, classification and formulation, review of previous work, and future opportunities. These considerations can guide the application of specific techniques to different domains to evaluate their effectiveness. Additionally, we discuss the computational complexity of these techniques, an important factor in real-world applications. Our goal is to provide a comprehensive understanding of the research conducted in this field and how techniques developed for one area can be adapted for use in other domains.
