A Deep Learning Model for Diagnosing Diabetic Retinopathy Using EfficientNet-B0

Diabetic Retinopathy (DR) is a severe ocular condition prevalent among individuals with diabetes, which can result in vision loss and ultimately blindness. This research presents an innovative approach to identify Diabetic Retinopathy (DR) using a Convolutional Neural Network (CNN) model known as EfficientNet-B0. Convolutional neural networks (CNNs), due to their capacity to efficiently analyze images, have a pivotal function in the diagnosis of diabetic retinopathy (DR). The model was trained using eye pictures from diabetic patients, depicting various phases of diabetic retinopathy (DR). The CNN successfully extracted significant information from the photos, allowing our model to reliably identify these stages. Our research demonstrates the efficacy of artificial intelligence, particularly convolutional neural networks (CNNs), in facilitating the timely identification and management of diabetic retinopathy (DR). The favorable outcomes derived from our model indicate its potential as a valuable instrument for ophthalmologists and promote additional investigation in this field.