SNAPSHOT INSIGHT TO MACHINE LEARNING METHODOLOGIES FOR EARLY DETECTION OF ONSET OF GLAUCOMA

Abstract

Early identification of glaucoma is a critical demand as well as prominent concern due to its risk of irreversible blindness if left unattended. Irrespective of presence of variable advanced imaging technologies powered by analytical operation, still there is no report of any benchmarked application or technology associated with it. In past half decade, machine learning algorithms and Artificial Intelligence (AI) has been showing some promising direction towards predictive analysis resulting in precise detection of early onset of glaucoma. However, there are manifold challenges associated with AI and learning algorithms too. Hence, this paper presents a compact snapshot of existing machine learning algorithms evolved recently in research area in order to identify the scale of effectiveness towards solving issue pertaining to early detection. The paper further contributes to briefing of frequently adopted dataset, highlighted updated research trends of publication along with exclusive learning outcomes and research gap.