Error Backpropagation Neural Network for Classifying Diabetic Diseases based on the Grading Method of Circular Region

Abstract

Diabetic retinopathy grading is an important issue after detecting lesions of retina to estimate their risk and to take a suitable decision for treatment. Here, the grading of diabetic retinopathy is examined by consistent medical approaches to build a computer model for grading in automated way, which improve the efficiency of diabetic screening services. After the grading of diabetic retinopathy, Error Backpropagation Neural Network Learning Rule is used to give suggestions to a doctor for suitable treatment for the patient. Here, sixteen different cases are trained, and it takes about 8.368 seconds with 20820 iterations. The Neural network diagnosis four-treatment cases and they are urgent, moderate, mild and normal. It is also found from the results that Neural Network is very fast algorithm to give these decisions. In addition, the program that is used for carrying out processes is MATLAB Program version 2015, the computer is HP core i7.