A Study of Diabetic Retinopathy Classification Using Support Vector Machine - Izzati Ab Kader, Nur; Kalsom Yusof, Umi; Naim, Syibrah (International Journal of Engineering and Technology, 2018)
Diabetic Retinopathy (DR) is a diabetic complication which can cause blindness. As DR cases keep increasing, ophthalmologists are forced to diagnose a large number of retinal images daily. Generally, the diabetic eye screening is done manually using qualitative scale to detect abnormalities on the retina. Although this approach is useful, the detection is not accurate; and create a need for a tool that can help the experts to classify the severity of DR to establish adequate therapy. Previous researchers have studied machine learning to propose an automatic DR classification. However, it needs to be improvised especially in terms of accuracy. Hence, this paper aimed to find classifier with optimal performance in the study of DR classification. This study considered three classes of diabetic patients which were patients who do not have DR (NODR), patients with non-proliferative DR (NPDR) and patients with proliferative DR (PDR), instead of focusing only on two classes (NO DR, DR). Support Vector Machine was used in this research due to the success of many classification problems that had been proposed which produced good result. The results obtained showed that SVM gave the best accuracy, 76.62% with average sensitivity of 0.8081 and average specificity of 0.8376 respectively.Â Â Â