CNN-Based Ocular Disease Identification in Retinal Images

Abstract

Abstract: Approximately 15 million people across India suffer from blindness, and unfortunately, 75% of these instances were treatable at one point in time. There are 10,000 patients for every doctor in India. Vision can be affected by a variety of eye diseases, such as cataracts, corneal ulcers, and trachoma, among others. Research indicates that untreated early-stage diseases are the primary causes of blindness in India. Only by receiving a proper diagnosis early on in the progression of these eye conditions can they be prevented. There are many different symptoms that can be seen with these eye conditions. Analyzing a wide variety of symptoms is necessary in order to make an appropriate diagnosis of eye conditions. With the use of deep learning techniques like convolution neural networks and digital image processing techniques like segmentation and morphology, we offer a novel approach to automatically identify eye diseases based on visually perceptible symptoms. The suggested approach is used to analyze and classify seven eye conditions: Diabetic retinopathy (DR), glaucoma, cataract, and age-related macular degeneration(AMD) are the most common ocular illnesses. Early detection of eye problems is facilitated by the proposed deep neural network model. In case that screening is necessary, the model advises people to contact an ophthalmologist.