A Pragmatic Glaucoma Detection Based On Deep Neural Network Strategy

Glaucoma is a group of diseases in which the nerve linking the eyes to the brain becomes destroyed, typically as a result of excessive intraocular pressure. The much more prevalent type of glaucoma diseases frequently manifests itself through gradual eyesight impairment. This kind of visual loss due to glaucoma is called open angle glaucoma and although this kind of angular glaucoma is uncommon, it is a clinical crisis with some indications such as eye pain and anxiety as well as acute vision disruption. In this paper, a new deep learning mechanism is used to identify the glaucoma disease in an intense manner, which is called Deep Neural Classification Network (DNCN). This proposed approach identifies the glaucoma disease efficiently by analyzing the retinal images with respect to two form factors such as Optical Coherence Tomography (OCT) and Retinal Fundus Images. The proposed DNCN model processes the retinal image based on the different processing procedures such as pre-processing, feature extraction, filtration and classification, in which it assures the accuracy ratio of 97.3% in outcome with the error rate of 0.27%.