Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Diabetic Retinopathy01:27

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

Correction: Shah et al. Visualization of Critical Limits and Critical Values Facilitates Interpretation. <i>Diagnostics</i> 2025, <i>15</i>, 604.

Diagnostics (Basel, Switzerland)·2026
Same author

High-Dimensional Flow Cytometry Identifies Immune Signatures Associated with Septic Shock Severity in Critically Ill Postoperative Patients.

Anaesthesia, critical care & pain medicine·2026
Same author

Integrated immune and endothelial profiling predicts 90-day mortality in postoperative sepsis and septic shock.

EBioMedicine·2026
Same author

Combined explainable deep learning model to predict pediatric sleep apnea from ECG and SpO<sub>2</sub>.

Measurement : journal of the International Measurement Confederation·2026
Same author

The potential of clustering methods for pre-test triage in sleep medicine: A systematic review.

Sleep medicine reviews·2026
Same author

An explainable deep learning approach for sleep staging in sleep apnea patients across all age subgroups from pulse oximetry signals.

Engineering applications of artificial intelligence·2026

Related Experiment Video

Updated: Jul 8, 2026

Using Retinal Imaging to Study Dementia
09:17

Using Retinal Imaging to Study Dementia

Published on: November 6, 2017

21.6K

Attention-based deep learning framework for automatic fundus image processing to aid in diabetic retinopathy grading.

Roberto Romero-Oraá1, María Herrero-Tudela2, María I López1

  • 1Biomedical Engineering Group, University of Valladolid, Valladolid, 47011, Spain; Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Spain.

Computer Methods and Programs in Biomedicine
|April 7, 2024
PubMed
Summary

This study introduces a novel deep learning framework for Diabetic Retinopathy (DR) grading, separating dark and bright retinal lesions for improved accuracy and explainability. The method generates interpretable attention maps, aiding clinicians in early DR detection and diagnosis.

Keywords:
Attention mechanismDeep learningDiabetic retinopathy gradingExplainable artificial intelligenceFundus images

More Related Videos

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.1K
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.7K

Related Experiment Videos

Last Updated: Jul 8, 2026

Using Retinal Imaging to Study Dementia
09:17

Using Retinal Imaging to Study Dementia

Published on: November 6, 2017

21.6K
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.1K
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.7K

Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Diabetic Retinopathy (DR) detection is crucial for preventing vision loss, but manual grading is time-consuming.
  • Automated DR grading systems often struggle with simultaneous detection of diverse lesion types.
  • Explainable AI (XAI) is needed to support clinical decisions in DR screening.

Purpose of the Study:

  • To develop an end-to-end deep learning framework for automatic DR grading into 5 severity levels.
  • To optimize DR classification by independently processing dark (red) and bright retinal lesions.
  • To provide clinicians with interpretable attention maps for enhanced diagnostic support.

Main Methods:

  • A novel attention mechanism was developed, decomposing retinal images to focus separately on dark and bright structures.
  • The framework incorporates image quality assessment, data augmentation, transfer learning, and fine-tuning.
  • The Xception architecture and focal loss function were utilized for feature extraction and handling data imbalance.

Main Results:

  • The proposed approach achieved 83.7% accuracy and a Quadratic Weighted Kappa of 0.78 in 5-class DR grading.
  • Independent attention maps were generated, distinguishing between red lesions (e.g., microaneurysms, hemorrhages) and bright lesions (e.g., hard exudates).
  • These attention maps offer crucial explainability for the model's predictions.

Conclusions:

  • The developed framework effectively automates Diabetic Retinopathy grading.
  • Separating attention for different lesion types optimizes classification performance.
  • Generated attention maps enhance visual interpretation, positioning the method as a valuable diagnostic aid for early DR detection.