Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Diabetic Retinopathy

Diabetic Retinopathy

DefinitionDiabetic retinopathy is a microvascular complication of diabetes affecting the retinal blood vessels.Risk FactorsDiabetic retinopathy is present in almost all individuals with type 1 diabetes and more than 60% of those with type 2 diabetes after two decades of disease.The risk increases with poor glycemic control, hypertension, dyslipidemia, smoking, pregnancy, and puberty.Although cataracts and glaucoma are also more frequent in people with diabetes, retinopathy remains the leading...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Toward AI-Assisted Sickle Cell Screening: A Controlled Comparison of CNN, Transformer, and Hybrid Architectures Using Public Blood-Smear Images.

Diagnostics (Basel, Switzerland)·2026

Same author

Correction: Alzahrani et al. Urolithin A and B Alter Cellular Metabolism and Induce Metabolites Associated with Apoptosis in Leukemic Cells. <i>Int. J. Mol. Sci.</i> 2021, <i>22</i>, 5465.

International journal of molecular sciences·2026

Same author

Lightweight Diffusion Models Based on Multi-Objective Evolutionary Neural Architecture Search.

International journal of neural systems·2025

Same author

Three-dimensional genome landscape of primary human cancers.

Nature genetics·2025

Same author

Boosted Harris Hawks Shuffled Shepherd Optimization Augmented Deep Learning based motor imagery classification for brain computer interface.

PloS one·2024

Same author

Identification of Anomalies in Lung and Colon Cancer Using Computer Vision-Based Swin Transformer with Ensemble Model on Histopathological Images.

Bioengineering (Basel, Switzerland)·2024

Same journal

RETRACTION: Framework to Segment and Evaluate Multiple Sclerosis Lesion in MRI Slices Using VGG-UNet.

Computational intelligence and neuroscience·2026

Same journal

RETRACTION: Facial Emotion Recognition Using a Novel Fusion of Convolutional Neural Network and Local Binary Pattern in Crime Investigation.

Computational intelligence and neuroscience·2026

Same journal

RETRACTION: Automatic Intelligent System Using Medical of Things for Multiple Sclerosis Detection.

Computational intelligence and neuroscience·2026

Same journal

RETRACTION: Intangible Cultural Heritage Reproduction and Revitalization: Value Feedback, Practice, and Exploration Based on the IPA Model.

Computational intelligence and neuroscience·2026

Same journal

RETRACTION: CNN Based Multiclass Brain Tumor Detection Using Medical Imaging.

Computational intelligence and neuroscience·2025

Same journal

RETRACTION: Distributed Scheduling Strategy of Virtual Power Plant Using the Particle Swarm Optimization Neural Network under Blockchain Background.

Computational intelligence and neuroscience·2025

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 8, 2026

Behavioral Assessment of Visual Function via Optomotor Response and Cognitive Function via Y-Maze in Diabetic Rats

Behavioral Assessment of Visual Function via Optomotor Response and Cognitive Function via Y-Maze in Diabetic Rats

Published on: October 23, 2020

Prediction of Diabetes through Retinal Images Using Deep Neural Network.

Mahmoud Ragab^1,2,3, Abdullah S Al-Malaise Al-Ghamdi^4,5,6, Bahjat Fakieh⁴

¹Information Technology Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia.

Computational Intelligence and Neuroscience

|June 13, 2022

Summary

This summary is machine-generated.

This study introduces a deep learning model using retinal images for early diabetes detection. The convolutional neural network achieved over 95% accuracy, offering a faster, automated screening method.

More Related Videos

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Retinal Pathophysiological Evaluation in a Rat Model

Retinal Pathophysiological Evaluation in a Rat Model

Published on: May 6, 2022

Related Experiment Videos

Last Updated: Jun 8, 2026

Behavioral Assessment of Visual Function via Optomotor Response and Cognitive Function via Y-Maze in Diabetic Rats

Behavioral Assessment of Visual Function via Optomotor Response and Cognitive Function via Y-Maze in Diabetic Rats

Published on: October 23, 2020

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Retinal Pathophysiological Evaluation in a Rat Model

Retinal Pathophysiological Evaluation in a Rat Model

Published on: May 6, 2022

Area of Science:

Ophthalmology
Medical Imaging
Artificial Intelligence

Background:

Diabetic retinopathy and macular edema are microvascular complications of diabetes affecting the retina.
Manual screening of retinal images for diabetes diagnosis is time-consuming.

Purpose of the Study:

To develop and evaluate a deep learning model for automated diabetes prediction using retinal images.
To automate the screening process for diabetic eye diseases.

Main Methods:

A 7-layer convolutional neural network (CNN) with ReLU activation and MaxPooling was designed.
Retinal image datasets were preprocessed and normalized for classification.
The CNN model was trained to classify images as diabetic or non-diabetic.

Main Results:

The proposed deep neural network achieved a training accuracy exceeding 95%.
Performance was evaluated using accuracy, precision, recall, and F1 score.
The model demonstrated superior performance compared to existing state-of-the-art algorithms.

Conclusions:

Deep learning, specifically CNNs, can effectively automate diabetes prediction from retinal images.
The developed model shows significant potential for improving early detection and screening of diabetic eye complications.