Evaluation of convolutional neural network variants for diagnosis of diabetic retinopathy

Abstract

Diabetic Retinopathy (DR) is a long-term complication of Diabetes Mellitus (DM) that impairs vision. This stage occurs in visual impairment and blindness if treated late. DR identified through scanning fundus images. A technique on classifying DR in fundus images is the deep learning approach, one of the methods of implementing machine learning. In this study, the Convolutional Neural Networks (CNN) method applied with the ResNet-50 and DenseNet-121 architectures. The data adopted in this analysis was generated from DIARETDB1, an online database containing fundus images. Then, the pre-processing stage is carried out on the fundus image to improve model performance, such as selected the green channel from the images and inverted it, converted the images into grayscale images, and applied Contrast Limited Adaptive Histogram Equalization (CLAHE) for uniform contrast in the images. The outcome of this research indicates that the ResNet-50 model is better than DenseNet-121 in detecting DR. The most reliable results from the ResNet-50 model's case testing are accuracy, precision, and recall of 95%, 98%, and 96% respectively.