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

BrainUMA: A Unified multi-atlas learning framework for brain disorders diagnosis.

Medical & biological engineering & computing·2026

Same author

C[Formula: see text]Net: A co-occurrence and consistency-aware framework for structured multi-label fundus diagnosis.

Medical & biological engineering & computing·2026

Same author

Quercetin attenuates nicotine-induced ovarian and uterine dysfunction in rats.

Clinical and experimental reproductive medicine·2026

Same author

One-year Weight Loss and Metabolic Outcomes of Single Anastomosis Sleeve Jejunal Bypass: A Retrospective Report with Reference to Roux-en-Y Gastric Bypass.

Obesity surgery·2025

Same author

An Efficient Transfer Learning With Prompt Learning for Brain Disorders Diagnosis.

IEEE journal of biomedical and health informatics·2025

Same author

Facial Artery Aneurysms: A Systematic Review of Characteristics, Therapeutic Interventions, and Clinical Results.

Journal of maxillofacial and oral surgery·2025

Search research articles

Related Experiment Video

Updated: May 6, 2026

Using Retinal Imaging to Study Dementia

Using Retinal Imaging to Study Dementia

Published on: November 6, 2017

Sharper Detection: Enhanced Techniques for Diabetic Retinopathy Grading.

Ali Rafiei¹, Osmar R Zaiane¹

¹University of Alberta, Edmonton, Canada.

Studies in Health Technology and Informatics

|August 8, 2025

Summary

Deep learning models significantly improved diabetic retinopathy (DR) grading accuracy using advanced techniques like oversampling and data augmentation. This enhances automated detection for preventing blindness.

Keywords:

Deep Learning Diabetic Retinopathy Model Improvement ResNet

More Related Videos

Retinal Pathophysiological Evaluation in a Rat Model

Retinal Pathophysiological Evaluation in a Rat Model

Published on: May 6, 2022

Preparation and Analysis of Histological Slides of Rat and Mouse Eyeballs to Evaluate the Retina

Preparation and Analysis of Histological Slides of Rat and Mouse Eyeballs to Evaluate the Retina

Published on: August 23, 2024

Related Experiment Videos

Last Updated: May 6, 2026

Using Retinal Imaging to Study Dementia

Using Retinal Imaging to Study Dementia

Published on: November 6, 2017

Retinal Pathophysiological Evaluation in a Rat Model

Retinal Pathophysiological Evaluation in a Rat Model

Published on: May 6, 2022

Preparation and Analysis of Histological Slides of Rat and Mouse Eyeballs to Evaluate the Retina

Preparation and Analysis of Histological Slides of Rat and Mouse Eyeballs to Evaluate the Retina

Published on: August 23, 2024

Area of Science:

Ophthalmology
Computer Science
Medical Imaging

Background:

Diabetic retinopathy (DR) is a primary cause of blindness globally.
Automated grading of DR using deep learning is crucial for timely intervention.
Existing methods often rely on basic data enhancement techniques.

Purpose of the Study:

To investigate the impact of advanced deep learning techniques on DR grading performance.
To enhance the accuracy of automated DR detection systems.
To establish a robust framework for improving DR classification.

Main Methods:

Applied oversampling, data augmentation, K-fold cross-validation, learning rate scheduling, and early stopping.
Utilized ResNet18 and ResNet50 deep learning models.
Trained and validated models on the APTOS and EyePACS datasets.

Main Results:

Achieved 97.78% accuracy on the APTOS dataset with ResNet18.
Attained 93.80% accuracy on the EyePACS dataset with ResNet50.
Demonstrated substantial accuracy improvements over baseline methods.

Conclusions:

Advanced deep learning techniques significantly boost automated DR grading accuracy.
The proposed framework offers a valuable enhancement to current state-of-the-art DR detection methods.
These improvements can aid in preventing vision loss due to diabetic retinopathy.