An Automatic Grading System of Severity Level for Diabetic Retinopathy Using Cnn Classifier

The human eye is an organ that reacts to light and has several purposes. As a sense organ, the human eye allows vision. The retina is a light-sensitive layer at the back of the eye that covers about 65% of its interior surface. Rodand cone cells in the retina allow conscious light perception and vision including colour differentiation and the perception of depth. A disease called diabetic retinopathy which is affected in the retina to the diabetic patients suffering with the diabetes more than 10 years. Diabetic Retinopathy (DR) is the utmost common diabetic eye disease and a leading reason of blindness. It is caused due to injury in the blood vessels of the retina. The brittle blood vessels may enlarge and leak fluid which is called as exudates. Exudates are fluids, cells, or other cellular substances that are slowly discharged from blood vessels usually from inflamed tissues. It can be a clear fluid which is composed of serum, fibrin, and white blood cells. Diabetic retinopathy is identified by the ophthalmologists by the dilation method. The dilation method is meant by pouring eye drops into the patient’s eye and it causes irritation to the patients. In order to protect the patients from the irritation and blindness, an automatic method is developed to detect these exudates and to measure the severity of the diabetic retinopathy. The exudates are detected by using the JSEG algorithm and the severity level grading is done by using Cascade Neural Network (CNN) classifier algorithm.