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ViResGF-Net: Gated Multi-Scale Hybrid Vision Transformer for Robust Fundus Image Multi-Label Classification.

Binghan Chen, Haolong Xiang, Jiayi Wan

    IEEE Journal of Biomedical and Health Informatics
    |November 24, 2025
    PubMed
    Summary

    A new AI model, ViResGF-Net, accurately classifies fundus diseases from eye images. This hybrid deep learning approach improves upon traditional methods, offering higher diagnostic accuracy for conditions like cataracts and glaucoma.

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    Area of Science:

    • Ophthalmology
    • Artificial Intelligence
    • Medical Imaging

    Background:

    • Global population aging increases the prevalence of visual impairment due to fundus diseases like cataracts and glaucoma.
    • Traditional ophthalmic diagnosis relies on subjective visual inspection of fundus images by doctors, leading to diagnostic discrepancies and challenges in identifying coexisting diseases.

    Purpose of the Study:

    • To develop an automated, accurate, and robust model for multi-class classification of fundus diseases.
    • To address the limitations of manual diagnosis, including inter-observer variability and specialization constraints.

    Main Methods:

    • Proposed ViResGF-Net, a gated multi-scale hybrid vision Transformer model integrating Convolutional Neural Network (CNN) and Vision Transformer (ViT) branches.
    • Employed a Feature Pyramid Network (FPN) to enhance local feature extraction within the CNN branch.

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  • Integrated features from both branches using a Gated Fusion Unit (GFU) before classification with a Multi-Layer Perceptron (MLP) classifier.

    • Main Results:

    • ViResGF-Net achieved high performance metrics: 93.56% accuracy, 92.99% precision, and 92.36% F1 score.
    • The model's performance surpassed existing methods in multi-class fundus disease classification.

    Conclusions:

    • The ViResGF-Net model demonstrates significant potential for improving the accuracy and efficiency of fundus disease diagnosis.
    • This hybrid deep learning approach offers a promising solution for automated ophthalmic image analysis, particularly in complex cases involving multiple pathologies.