MODELING AND ANALYSIS OF IDENTIFICATION OF OCULAR DISEASES
India has a visually impaired population of around 15 million. The specialist tolerant proportion in India is 1:10,000. Early visual infection location is a financial and compelling approach to prevent visual deficiency caused by, glaucoma, cataract,uveitis and numerous different ocular diseases. Glaucoma makes harms the optic nerve, along these lines prompts visual deficiency. The beginning phases of the diseases are asymptomatic, making its discovery troublesome, and whenever left untreated, this can make irreversible harms vision. Cataract has been archived to be the most huge reason for reciprocal visual impairment in India where vision < 20/200 in the better eye on introduction is characterized as blindness. In India has been accounted for to be liable for 50-80% of the respectively visually impaired in the nation. Uveitis is a complex multifactorial immune system sickness. The four anatomical kinds of uveitis incorporate foremost uveitis, moderate uveitis, back uveitis and uveitics. The greater part of the uveitic substances is idiopathic. The ailment is more normal in persons with a mean age gathering of 35 to 45 years. The disease can cause shifting degrees of visual misfortune. In the US, uveitis has been distinguished as a reason for 10% of the legitimate visual impairment or examinations done make it a costly as well as time consuming process. Along these lines, this should be possible by using Deep Learning(where Deep Learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks)w. Hence, we have made a model that groups the various ailments of eyes, for example, Cataract, glaucoma and uveitis based on fundus images. The model depends on CNN(Convolution Neural Network) which is a class of deep neural networks, most commonly applied to analyzing visual imagery and characterizes 3 unique sorts of eye illnesses. The accuracy that has been accomplished is 85%.