Diabetic Retinopathy Detection using Deep Convolutional Neural Network with Visualization of Guided Grad-CA

One of the complications of diabetes that represents a serious threat to world health is Diabetic Retinopathy (DR). High blood sugar levels in people with diabetes can damage the blood vessels in the retina and causing blindness. DR can be detected by examining the fundus image by an ophthalmologist. However, the limited number of ophthalmologists who can analyze fundus image is an obstacle because the number of DR sufferers continues to increase. Therefore, an automated system is needed to help doctors diagnose the disease. Researchers have developed deep learning techniques as Artificial Intelligence (AI) approach to finding DR in fundus images. In this research, we use the Deep Convolutional Neural Networks method with InceptionV3 structure and various optimizers such as the Stochastic Gradient Descent with Momentum (SGDM), Root Mean Square Propagation (RMSprop), and Adaptive Moment Estimation (Adam). The fundus image dataset previously through the augmentation and preprocessing steps to make it easier for the model to recognize the image. The InceptionV3 model with the Adam optimizer gave the best results in detecting DR lesions from the Kaggle dataset with 96% accuracy. This paper also presents a Grad-CAM guided activation map that can describe the position of the suspicious lesion to explain the results of DR detection.