An Effective Diabetic Retinopathy Detection Using Hybrid Convolutional Neural Network Models

Abstract

Loss of vision in the present era of the developing world is mainly caused by diabetic retinopathy. More than 103 million people are believed to be affected. It is estimated that around 40 million beings have diabetes in the United States, and according to the World Health Organization (WHO), 347 million people are living with the disease globally. Diabetic retinopathy (DR) is a long-term diabetes-related eye condition. Roughly, 45–50% of the American citizens suffering from diabetes undergo some unique stages that can be categorized. When DR is diagnosed on a timely basis, the possibility of it extending to the course of vision impairment can be delayed and stopped, though this is not entirely true and a very daunting task because it seldom reveals any symptom before it escalates to a stage of no return to effectively treat it. The paper uses convolutional neural network models to achieve an effective classification for diabetic detection of retinal fundus images. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.