A Two-Stage Neural Network Framework for Automated Diabetic Retinopathy Detection

Abstract

Diabetic Retinopathy (DR) is a serious complication of diabetes that affects the retina
and can lead to vision impairment or blindness if not detected early. This condition can be
diagnosed by analyzing retinal images captured using a fundus camera. With advancements in
artificial intelligence, particularly in deep learning, Diabetic Neural Networks (DNNs) have
emerged as a powerful tool in biomedical signal analysis. These networks not only model
complex image features but also effectively classify retinal images as normal or indicative of
diabetic retinopathy. The automatic and accurate detection of DR plays a crucial role in timely
intervention and can significantly reduce the risk of vision loss among diabetic patients.