Fundus Image Based Early Glaucoma Detection Using Deep Learning

Glaucoma is a chronic eye disease that leads to irreversible vision loss. Vision loss from glaucoma cannot be reversed, early detection, thus, is essential to preserve vision and life quality. It is necessary to detect Glaucoma at early stages in order to avoid vision loss without visiting the medical experts we can achieve this through proposed method. Clinically, there are three mainly examinations practiced to screen glaucoma: intraocular pressure (IOP) measurement, function-based visual field test, and optic nerve head (ONH) assessment. However said methods are cost effective and time taking processes. Recently, deep learning approaches have evolved in to the glaucoma detection. To address the glaucoma issue, by employing ResNet-50 architecture on the ACRIMA, RIM-ONE, ORIGA, and DRISHTI-GS dataset. The proposed model may help clinicians to diagnose early-stage glaucoma with very high accuracy for timely interventions