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

Search research articles

Related Experiment Video

Updated: Jun 11, 2026

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

A hybrid Transformer-CNN framework for uncertainty-guided semi-supervised multiclass eye disease classification with

Muhammad Hammad Malik¹, Zishuo Wan¹, Yingying Ren²

¹School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China.

Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society

|January 11, 2026

Summary

Related Concept Videos

Classification of Illness

Classification of Illness

The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe and...

You might also read

Related Articles

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

Sort by

Same author

Seasonal summer stress affects systemic redox homeostasis: a longitudinal marker analysis in healthy adults.

Frontiers in public health·2026

Same author

Metabolite-Based Biomarkers for Autism Spectrum Disorder: Current Research Progress.

Current molecular medicine·2026

Same author

Understanding hydrogen reactivity with anhydrite in underground hydrogen storage.

Scientific reports·2026

Same author

Potential risk assessment of different sizes of microplastics on the digestive system of hybrid sturgeon.

Environmental geochemistry and health·2026

Same author

Extraordinary diversity of antiviral immune-related gene families across 38 bat species.

BMC biology·2026

Same author

Novel Angiotensin-Converting Enzyme Inhibitory Peptides from <i>Bungarus multicinctus</i>: Simulated Gastrointestinal Digestion, Identification and Antihypertensive Mechanism.

Pharmaceuticals (Basel, Switzerland)·2026

This study introduces a novel AI model for classifying eye diseases from fundus images, achieving high accuracy and interpretability. The approach enhances early diagnosis and treatment to prevent vision loss.

Area of Science:

Ophthalmology and Medical Imaging
Artificial Intelligence in Healthcare
Deep Learning for Disease Classification

Background:

Accurate classification of eye diseases like cataract, diabetic retinopathy (DR), and glaucoma from fundus images is crucial for preventing vision loss.
Existing deep learning methods face challenges with large labeled datasets, inefficient unlabeled data utilization, and limited interpretability, hindering clinical application.

Purpose of the Study:

To develop a novel CNN-Transformer hybrid architecture for enhanced multiclass eye disease classification.
To improve the utilization of both labeled and unlabeled data through innovative semi-supervised learning (SSL).
To enhance model interpretability for clinical validation and trust.

Main Methods:

A hybrid CNN-Transformer architecture (ConvNeXt backbone with Transformer modules) using multi-head attention for spatial and long-range feature capture.

Keywords:

CNN Transformer hybrid architecture Clinical interpretability Eye disease classification Gradient integrated attention maps Uncertainty-guided semi-supervised learning

Related Experiment Videos

Last Updated: Jun 11, 2026

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

Uncertainty-Guided MixMatch (UG-MixMatch) SSL framework employing Monte Carlo (MC) dropout for uncertainty quantification and pseudo-label refinement.

Gradient-based Integrated Attention Map (GIAM) for interpretable predictions, aggregating attention maps with adaptive channel-wise weighting.

Main Results:

Achieved 95.27% classification accuracy with UG-MixMatch and 95.51% with MC dropout on the Ocular Imaging Health (OIH) dataset.
Demonstrated near-perfect agreement with ground truth (Cohen's kappa score of 93.70).
Exceptional class-wise performance, including 100% sensitivity/specificity for DR and high specificity for cataract and glaucoma, with robust AUC values.

Conclusions:

The proposed framework effectively addresses data scarcity and enhances interpretability in eye disease classification.
The hybrid model delivers clinically relevant performance, offering a promising step towards scalable, explainable, and accurate diagnostic tools.
GIAM visualizations provide enhanced clinical interpretability, validating model predictions for potential use in Clinical Decision Support Systems (CDSS).