CLASSIFICATION OF DIABETIC RETINOPATHY USING IMAGE PROCESSING IN DIABETIC PATIENTS

Diabetic retinopathy is a retinal condition that affects people with diabetes and is the leading cause of blindness in the elderly. It's an asymptomatic illness characterized by abnormalities in blood vessels that might cause them to bleed or leak fluid, resulting in visual distortion. As a result, blood vessel extraction is critical in assisting ophthalmologists in detecting this illness at an early stage and preventing vision loss. Diabetes Retinopathy (DR) is a debilitating chronic illness that is one of the primary causes of blindness and vision impairment in diabetic individuals in industrialized nations. According to studies, the majority of instances may be avoided with early identification and treatment. Physicians utilize retinal imaging to detect lesions associated with this illness during eye screening. The amount of pictures that must be manually examined is getting expensive because of the rising number of diabetics.. In this research, we used Image Processing to offer a technique for automatically classifying diabetic retinopathy disease based on retina fundus pictures. For this, we combined a feature extraction approach based on a pre-trained deep neural network model with a machine learning-based support vector machine classification algorithm. In MATLAB software, the proposed system is examined and analyzed.