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

You might also read

Related Articles

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

Sort by

Same author

Multi-Scale Attention Fusion With Depthwise Separable Convolutions for Efficient Skin Cancer Detection.

Journal of cutaneous pathology·2025

Same author

Explainable deep learning approaches for high precision early melanoma detection using dermoscopic images.

Scientific reports·2025

Same author

InsightNet: A Deep Learning Framework for Enhanced Plant Disease Detection and Explainable Insights.

Plant direct·2025

Same author

Enhancing Typhoid Fever Diagnosis Based on Clinical Data Using a Lightweight Machine Learning Metamodel.

Diagnostics (Basel, Switzerland)·2025

Same author

LeafDNet: Transforming Leaf Disease Diagnosis Through Deep Transfer Learning.

Plant direct·2025

Same author

Revolutionizing Brain Tumor Detection Using Explainable AI in MRI Images.

NMR in biomedicine·2025

Same journal

Driving Innovation: Transatlantic Attitudes to the <i>Bionics Bus</i> as a Vehicle for Health Transformation and STEM Engagement.

Healthcare technology letters·2026

Same journal

Gamified Digital Solutions for Tinnitus Health Literacy: The Erasmus+ Project TinWise.

Healthcare technology letters·2026

Same journal

Effect of Technology-Supported Measures Used for Care Transition Decisions for Chronic Disease Patients: A Systematic Review and Meta-Analysis.

Healthcare technology letters·2026

Same journal

Bibliometric Trends in the Integration of Computer Vision With Healthcare.

Healthcare technology letters·2026

Same journal

Parameter-Efficient Deep Learning Models for Vital Sign Estimation From PPG.

Healthcare technology letters·2026

Same journal

Machine Learning-Based Depression Recognition With Preserved Efficacy From Compressed EEG Signals Using Wavelet Transform and Adaptive Filtering.

Healthcare technology letters·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Feb 26, 2026

Integrated Photoacoustic Ophthalmoscopy and Spectral-domain Optical Coherence Tomography

Integrated Photoacoustic Ophthalmoscopy and Spectral-domain Optical Coherence Tomography

Published on: January 15, 2013

OpthaNet: Attention-Integrated Architecture for High-Precision Multi-Class Ophthalmic Image Classification.

Souhardo Rahman¹, Md Nasif Safwan¹, Mahamodul Hasan Mahadi¹

¹Department of Computer Science American International University-Bangladesh Dhaka Bangladesh.

Healthcare Technology Letters

|February 25, 2026

Summary

This summary is machine-generated.

This study compared deep learning models for classifying eye diseases like cataracts, diabetic retinopathy, and glaucoma. Optimized models showed significant accuracy improvements, demonstrating AI

Keywords:

deep learning diabetic retinopathy efficientNet eye disease classification transfer learning

More Related Videos

Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping

Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping

Published on: December 8, 2023

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

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

Published on: November 30, 2022

Related Experiment Videos

Last Updated: Feb 26, 2026

Integrated Photoacoustic Ophthalmoscopy and Spectral-domain Optical Coherence Tomography

Integrated Photoacoustic Ophthalmoscopy and Spectral-domain Optical Coherence Tomography

Published on: January 15, 2013

Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping

Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping

Published on: December 8, 2023

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

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

Published on: November 30, 2022

Area of Science:

Ophthalmic diagnostics
Artificial intelligence in healthcare
Medical imaging analysis

Background:

Deep learning models, including CNNs and transformers, are increasingly used in ophthalmic diagnostics.
A direct comparison of these models for multi-class eye disease classification is limited.
High-performing systems often require substantial computational resources, posing challenges for practical screening.

Purpose of the Study:

To investigate and compare the efficacy of pre-trained deep learning models for multi-class classification of cataract, diabetic retinopathy, and glaucoma.
To address practical bottlenecks in ophthalmic transfer learning, such as feature selectivity and overfitting with limited data.
To evaluate tailored modifications for EfficientNetB3, MobileNetV2, and Vision Transformer models.

Main Methods:

Evaluation of EfficientNetB3, MobileNetV2, and Vision Transformer models with specific customizations.
Implementation of an attention-enhanced feature refinement module and OpthaHead classifier for EfficientNetB3 and MobileNetV2.
Application of META customization to optimize the Vision Transformer model.
Training and validation using fundus images for multi-class classification of eye diseases.

Main Results:

Optimized EfficientNetB3 achieved 96.04% accuracy, a 10.84% improvement over baseline.
Optimized MobileNetV2 showed an 11.26% improvement, balancing accuracy and computational efficiency.
META-customized Vision Transformer performance increased by over 18%, indicating benefits of reduced complexity on limited medical data.

Conclusions:

AI-driven classification demonstrates strong performance for detecting common eye diseases.
Tailored model modifications can significantly enhance accuracy and efficiency in ophthalmic diagnostics.
AI tools hold substantial potential for early eye disease detection, improving clinical decisions and patient outcomes.