Identification of Diabetic Retinopathy with Retinal Fundus Imagery Using Probabilistic Neural Network

Abstract

Abstract Diabetic retinopathy is a microvascular complication of diabetes mellitus that attacks blood vessels in the retina. The main characteristics of diabetic retinopathy are microaneurysm, retinal haemorrhages, exudates, and neovascularization. One of the methods used to diagnose diabetic retinopathy is by examining the retinal fundus image. The examination is still done manually by an ophthalmologist. Manual examination requires a high level or concentration and misidentification may occur because some diabetic retinopathy characteristics are difficult to see directly, so it is needed a method that can facilitate the ophthalmologist in making decisions to identify diabetic retinopathy. The method proposed in this research is Probabilistic Neural Network to identify diabetes retinopathy. Before the identification stage is carried out, the retinal image will go through the pre-processing stage in the form of resize, green channel, contrast stretching and feature extraction using the Gray Level Co-Occurrence Matrix. After testing in this research, it was concluded that the proposed method was able to identify diabetes retinopathy with an accuracy of 86.8%.