Glaucoma is a most dangerous and irreversible disease for eye. It clues to corrosion in visualisation and quality of life. Diagnosing and detecting of Glaucoma in proper time is difficult. Previously, many researchers uses data mining based analysis for analysis of glaucoma images for detecting of glaucoma patients. In this paper, we build up a deep learning (DL) engineering with convolutional neural network for robotized glaucoma determination. Profound learning frameworks, for example, convolutional neural networks (CNNs), can derive a progressive portrayal of images to segregate among glaucoma and non-glaucoma designs for symptomatic choices. Dropout and information growth procedures are received to further lift the presentation of glaucoma analysis.
Volume 11 | Issue 10