MaxGRNet: A multi-axis vision transformer with improved generalization for eye disease classification using explainable AI with insertion-deletion operations on fundus images

Clinical Snapshot

Key Findings

• P Value: Statistically significant (exact p-values not provided)

• Effect Size: 96.75% macro-averaged test accuracy

• Primary Outcome: Multi-class eye disease classification accuracy

• Nnt Or Sensitivity: Precision: 96.70%, Recall: 96.80% (individual disease sensitivities not reported)

• Confidence Interval: Not reported

Clinical Application

Requires high-quality fundus imaging equipment and computational infrastructure for transformer model deployment Could support RACGP diabetic retinopathy screening guidelines and reduce specialist referral burden, but requires TGA validation for clinical deployment and integration with existing telehealth infrastructure Patients undergoing fundus photography for diabetic retinopathy, glaucoma, and cataract screening

Abstract

Eye diseases, including diabetic retinopathy (DR), glaucoma, and cataracts, represent a major global health concern and can lead to severe visual impairment or blindness if not identified in a timely manner. This study proposes a novel eye disease classification framework based on a multi-axis vision transformer (MaxViT) applied to color fundus images with Explainable Artificial Intelligence (XAI) techniques to enhance model transparency. The proposed architecture integrates transformer-based attention mechanisms with Global Response Normalization (GRN)-based multi-layer perceptron (MLP) layers to capture complex spatial and contextual relationships within fundus images effectively. The model was evaluated on a publicly available eye disease classification dataset using a five-fold cross-validation strategy to assess its robustness and generalization. The experimental results show that the proposed approach consistently outperforms conventional Convolutional Neural Networks (CNNs) and Vision Transformer (ViT) variants, including ResNet50, Swin-T, MaxViT-T, and ViT-B16. The model achieved a macro-averaged test accuracy, precision, and recall values of 96.75%, 96.70%, and 96.80%, respectively, with paired statistical t-tests confirming that these improvements were statistically significant. Rigorous preprocessing techniques were employed to improve data consistency, and XAI-based visual explanations provided insights into the model's decision-making process, supporting interpretability in ophthalmic image analysis. Overall, the proposed MaxViT-based framework is robust and computationally feasible for research-oriented evaluation approaches for automated fundus image classification, highlighting the potential of advanced transformer architectures for future decision-support and research-oriented ophthalmic applications.

References

1. 1.Santo, M. H., Bhoyan, F. H., Farhad, F. I. J., Farid, F. A., Chakraborty, S., Mehedi, M. H. K., Uddin, J., & Karim, H. B. A. (2024). MaxGRNet: A multi-axis vision transformer with improved generalization for eye disease classification using explainable AI with insertion-deletion operations on fundus images. PLoS One, 19(8), e0338415. https://doi.org/10.1371/journal.pone.0338415