An Early diagnosis of diabetic retinopathy using ConvNeXt

Diabetic retinopathy (DR), a frequent complication of diabetes mellitus, is the prime reason for vision loss worldwide. The current method for identifying diabetic retinopathy is a time-consuming and manual process, requiring trained medical professionals to examine digital color fundus photographs of the retina and look for the presence of lesions associated with the disease’s vascular abnormalities. Despite technological advancements, accurately identifying the early stages of diabetic retinopathy remains a challenge. To address this issue, different computer vision-based approaches have been introduced to detect DR and classify various stages from retina images. However, these techniques have limited success, particularly in accurately identifying the early stages of the disease. In this study, we employed a publicly available Kaggle Eyepac dataset to train our model for the detection of DR which is known to have five distinct stages, namely, proliferate DR, severe DR, moderate DR, mild DR, and no DR. The overall accuracy, precision, recall, Fl-score, and AUC score obtained by our proposed model were 88.31± 0.23,89.15± 0.19,90.25± 0.41,89.69± 0.25, and 87.38± 1.45 respectively. The performance of our model is improved by employing a pre-processing technique known as Contrast Limited Adaptive Histogram Equalization (CLAHE) followed by a state-of-the-art deep learning model known as ConvNeXt to achieve promising results.