Abstract: Diabetic retinopathy (DR) remains a critical concern in the realm of ophthalmology, demanding accurate and efficient diagnostic methodologies. This paper presents a comprehensive approach ...

The evaluation of retinal images using an algorithm based on deep machine learning can improve early detection and treatment of diabetic retinopathy, claims research. Adults with type 2 diabetes ...

Diabetic retinopathy (DR) is one of the common chronic complications of diabetes and the most common blinding eye disease. If not treated in time, it might lead to visual impairment and even blindness ...

Objective: To systematically review and meta-analyze the effectiveness of deep learning algorithms applied to optical ... DL shows relatively high accuracy for detection of diabetic retinopathy, ...

Published by Elsevier Ltd. We created and validated two versions of a deep ... “diabetic retinopathy screening programs”, “automated detection of diabetic retinopathy using fundus photographs”, ...

This research work proposes a comprehensive methodology for the automated detection, grading, and segmentation of DR, leveraging advanced image processing , deep learning techniques and machine ...

This project is a comprehensive web application for Diabetic Retinopathy (DR) detection using a Convolutional Neural Network (CNN) algorithm, developed with Django. The system includes a chatbot for ...

This project focuses on detecting the stages of Diabetic Retinopathy using deep learning models and comparing the performance of two powerful algorithms: EfficientNetB3 and EfficientNetB4. Abstract ...