Hybrid Deep Learning Network to Classify Eye Diseases

Early identification and ongoing monitoring of individuals with eye illnesses have been key concerns in computer- assisted detection. In computer aid screens, detecting one or more specific types of eye disease has achieved a big advance. Glaucoma is a collection of eye illnesses that will damage theoptic nerve, which is sole responsible for information sharing between eye to the brain, and can lead to blindness if left untreated. Glaucoma must be detected as early as possible for proper treatment. Diabetic retinopathy is also an eye illness due to diabetes which in turn harm the blood vessels of retina. This system proposed a Hybrid Ensemble Deep Learning system for early detection of Glaucoma and diabetic retinopathy. Eye images are first preprocessed to make them ready for subsequent processing. Based on the features retrieved during training, the system classifies incoming input images as normal eye, glaucoma afflicted eye, or diabetic retinopathy affected eye. This system produces 95% of accurate and stable output when compared to existing system.