An Automated Multimodal Glaucoma Detection Framework Using ViT and a Stacking-Based Ensemble

• URL: http://arxiv.org/abs/2607.02692v1
• Date: Thu, 02 Jul 2026 18:33:04 GMT
• Title: An Automated Multimodal Glaucoma Detection Framework Using ViT and a Stacking-Based Ensemble
• Abstract summary: Glaucoma is a progressive eye disease that can lead to irreversible vision loss if not detected at an early stage.<n>In this study, glaucoma detection is investigated under two evaluation settings: sample-wise, and patient-wise.<n>An automated multimodal framework is proposed that integrates fundus images with clinical data.
• Score: 2.5937996810126407
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Glaucoma is a progressive eye disease that can lead to irreversible vision loss if not detected at an early stage. Conventional diagnostic procedures are often time-consuming and rely heavily on expert interpretation, limiting their scalability for large-scale screening. In this study, glaucoma detection is investigated under two evaluation settings: sample-wise, where individual samples are analyzed independently, and patient-wise, where data from each patient are aggregated for final prediction. An automated multimodal framework is proposed that integrates fundus images with clinical data. Under the sample-wise setting, detection is performed using fundus images and clinical features individually, as well as through their multimodal combination. Under the patient-wise setting, predictions are obtained by aggregating multiple fundus image representations with corresponding clinical information for each patient. Deep visual features are extracted using a Vision Transformer (ViT) architecture and classified using classical machine-learning models, with a stacking-based ensemble of the three best-performing classifiers employed to optimize performance. Experiments conducted on the publicly available PAPILA dataset demonstrate strong diagnostic performance, achieving 97.47% accuracy and a 97.50% F1-score for sample-wise multimodal classification, and 98.97% accuracy and F1-score for subject-wise detection. The proposed framework is further deployed as an end-to-end web-based platform to support automated glaucoma screening and clinical decision support.

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