Automated Glaucoma Classification in Fundus Images Using Multi-Backbone Feature Fusion

초록

Glaucoma is one of the leading causes of irreversible blindness worldwide, underscoring the need for accurate and accessible diagnostic solutions. In this study, we propose a scalable glaucoma classification pipeline that leverages deep features extracted from sensor-acquired retinal fundus images using pretrained architectures-Swin Transformer V2, ConvNeXt V2, and EfficientNet V2. These models are employed as fixed feature extractors, and the concatenated representations are subsequently used to train and evaluate classical classifiers, including Random Forest, Multilayer Perceptron, and Support Vector Machine (SVM). Among these approaches, the SVM achieved the most balanced performance, which was further improved through hyperparameter optimization. Experiments conducted on the public RIM-ONE DL dataset demonstrated that the optimized SVM attained an accuracy of 0.957, an F1-score of 0.938, and an AUC-ROC of 0.978. These results highlight the effectiveness of integrating transformer-based and CNN-based deep representations with conventional machine learning models, offering a practical and resource-efficient framework for automated glaucoma classification.

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