An efficient novel approach for glaucoma classification on retinal fundus images through machine learning paradigm

Abstract

Glaucoma, a neuro-degenerative eye disease, is the result of an increase in intraocular pressure inside the retina. It is the second-leading cause of blindness worldwide, and if an early diagnosis is not made, it can lead to total blindness. There is a critical need to develop a system that can work well without a lot of equipment, qualified medical professionals, and requires less time about this core issue. This article provides a thorough examination of the main machine learning (ML) techniques employed in the processing of retinal images for the identification and diagnosis of glaucoma. Machine learning (ML) has been demonstrated to be a crucial technique for the development of computer-assisted technology. Machine learning (ML) techniques can be used to construct predictive models for the early diagnosis of glaucoma. Our objective is to develop a machine learning algorithm that can accurately forecast the likelihood of developing glaucoma using patient data. Ophthalmologists have also conducted a significant amount of secondary research over the years. Such characteristics emphasise the importance of ML while analysing retinal pictures.