Development of Automated System for Detection of Diabetic Retinopathy

The problem of people with diabetes causes a condition known as Diabetic Retinopathy (DR). It is most common in the elderly age peoples. As diabetes progresses, patients' perceptions may begin to deteriorate and cause DR. People lose their sight because of this disease. To deal with DR, early detection is required. Patients will have to be examined by doctors regularly which is a waste of time and energy. In recent time, number of DR cases are increasing exponentially due the modern stress life style. Therefore, it is necessary to automate the detection and diagnosis process of the DR. In this paper, machine learning (ML) techniques are used to diagnose early DR. These are the Probabilistic Neural Network (PNN), Support Vector Machine (SVM), Bayesian Separation and K-Means Clustering. These methods will be tested and compared with choosing the best method. 3000 images being processed for training and testing. Features are extracted from these fundus images using image processing techniques. After research, the results confirm that SVM is the best way to detect DR early with a higher level of accuracy compared to other classifiers.