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

You might also read

Related Articles

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

Sort by
Same author

Machine-Learning-Based Disease Diagnosis and Prediction: Progress, Perspectives, and the Path Forward.

Diagnostics (Basel, Switzerland)·2026
Same author

An explainable multi-stage framework for brain tumor classification using hybrid feature fusion and EfficientNetB5 model.

Scientific reports·2026
Same author

A gated task-attentive multi-task network for unified retinal image analysis.

Scientific reports·2026
Same author

Morphology-guided attention networks for explainable skin cancer detection under clinical uncertainty.

Frontiers in oncology·2026
Same author

Explainable and uncertainty-aware ensemble framework with causal analysis for breast cancer detection.

Frontiers in oncology·2026
Same author

An explainable deep learning-based feature fusion model for acute lymphoblastic leukemia diagnosis and severity assessment.

Frontiers in medicine·2026

Related Experiment Video

Updated: Jun 5, 2025

Author Spotlight: Ex Vivo OCT-Based Multimodal Imaging of Human Donor Eyes for Research into Age-Related Macular Degeneration
10:14

Author Spotlight: Ex Vivo OCT-Based Multimodal Imaging of Human Donor Eyes for Research into Age-Related Macular Degeneration

Published on: May 26, 2023

3.0K

CAD-EYE: An Automated System for Multi-Eye Disease Classification Using Feature Fusion with Deep Learning Models and

Maimoona Khalid1, Muhammad Zaheer Sajid1, Ayman Youssef2

  • 1Department of Computer Software Engineering, Military College of Signals, National University of Science and Technology, Islamabad 44000, Pakistan.

Diagnostics (Basel, Switzerland)
|December 17, 2024
PubMed
Summary
This summary is machine-generated.

A new AI tool, CAD-EYE, accurately classifies major eye diseases like diabetic retinopathy and glaucoma using deep learning. This advanced system aids medical professionals in early diagnosis, improving patient outcomes and preventing vision loss.

Keywords:
deep learningfeature fusionmulti-eye disease

More Related Videos

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
Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition
09:37

Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition

Published on: August 18, 2022

2.2K

Related Experiment Videos

Last Updated: Jun 5, 2025

Author Spotlight: Ex Vivo OCT-Based Multimodal Imaging of Human Donor Eyes for Research into Age-Related Macular Degeneration
10:14

Author Spotlight: Ex Vivo OCT-Based Multimodal Imaging of Human Donor Eyes for Research into Age-Related Macular Degeneration

Published on: May 26, 2023

3.0K
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
Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition
09:37

Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition

Published on: August 18, 2022

2.2K

Area of Science:

  • Ophthalmology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Diabetic retinopathy, hypertensive retinopathy, glaucoma, and contrast-related eye diseases are significant causes of vision impairment.
  • Early detection of these conditions is crucial for preventing severe consequences such as visual nerve damage and blindness.
  • Deep learning and AI offer promising avenues for improving the accuracy and efficiency of early eye disease diagnosis.

Purpose of the Study:

  • To introduce CAD-EYE, a novel AI-driven methodology for classifying diabetic retinopathy, hypertensive retinopathy, glaucoma, and contrast-related eye issues.
  • To enhance diagnostic accuracy and efficiency through advanced feature fusion and image processing techniques.

Main Methods:

  • The CAD-EYE system employs feature fusion from two deep learning models, MobileNet and EfficientNet, for enhanced diagnostic performance.
  • Fluorescence imaging is integrated as an image processing algorithm to improve accuracy and provide interpretability.
  • The system was trained on a large dataset of 65,871 fundus images sourced from reputable online platforms.

Main Results:

  • The CAD-EYE system achieved a remarkable 98% classification accuracy in identifying various eye diseases.
  • Comparative analysis confirmed that CAD-EYE outperforms established models like ResNet, GoogLeNet, VGGNet, InceptionV3, and Xception.
  • The results demonstrate superior performance compared to existing state-of-the-art methods in the literature.

Conclusions:

  • The findings validate CAD-EYE as a valuable diagnostic tool for medical professionals in identifying eye diseases.
  • While CAD-EYE significantly aids in diagnosis, it is intended to augment, not replace, the expertise of optometrists.
  • The study highlights the potential of AI in revolutionizing ophthalmic diagnostics and patient care.