Grade Classification of Diabetic Retinopathy Based on Single Model Convolutional Neural Network - Desti Fitriati; Sri Rezeki Candra Nursari (Universitas Methodist Indonesia, 2025)
Diabetes Mellitus (DM) is one of the diseases that has attracted global attention because it ranks fourth as a non-communicable disease with the highest mortality rate after cardiovascular, cancer, chronic respiratory diseases. DR is a condition caused by diabetes that can cause permanent damage to the blood vessels of the retina which can lead to blindness. DR is divided into 2 stages, namely non-proliferative diabetic retinopathy (NPDR) and proliferative diabetic retinopathy (DR), where each stage has different characteristics. From several studies that have been conducted previously, Convolutional Neural Network (CNN) has been widely used in recent years to segment medical images with remarkably consistent results. However, it is still necessary to find a suitable model to be able to adapt to all existing variables. For this reason, this study proposes a method as a modified model of CNN using seven layer. From the results of the research conducted, the proposed method uses four class models, namely 5 classes, 3 classes, 2 classes (Healthy & DR), and 2 classes (Healthy & Moderate). This research produced accuracy rates of 52%, 68%, 92% and 84% respectively.