Enhancing Accuracy and Efficiency in Diabetic Retinopathy Detection: A Deep Learning Framework for Fundus Image Analysis

The present research investigates the challenge of accurately and efficiently diagnosing diabetic retinopathy using a deep learning architecture. Our research is based on a sizable dataset of 54,325 fundus images that have been meticulously classified as diabetic retinopathy (DR) positive or negative by medical professionals. The dataset is carefully split into training and validation subsets, with 80% designated for training and 20% for validation, to ensure the robustness of model performance. Convolutional neural networks (CNN), artificial neural networks (ANN), support vector machines (SVM), recurrent neural networks (RNN), and k-nearest neighbors (KNN) are just a few of the machine learning models whose diagnostic performance we examine. CNN came out on top with a precision rate of 98.66% and excellent standards for recall, precision, and F1 score. The results of this study have the potential to significantly improve patient care and treatment by allowing for a quick and precise identification of diabetic retinopathy. The outcomes also demonstrate how medical image processing and diagnosis can be revolutionized by deep learning frameworks.