Spatial-aware Transformer-GRU Framework for Enhanced Glaucoma Diagnosis from 3D OCT Imaging

• URL: http://arxiv.org/abs/2403.05702v2
• Date: Thu, 04 Sep 2025 09:22:57 GMT
• Title: Spatial-aware Transformer-GRU Framework for Enhanced Glaucoma Diagnosis from 3D OCT Imaging
• Authors: Mona Ashtari-Majlan, David Masip,
• Abstract summary: We present a novel deep learning framework that leverages the diagnostic value of 3D Optical Coherence Tomography ( OCT) imaging for automated glaucoma detection.<n>We integrate a pre-trained Vision Transformer on retinal data for rich slice-wise feature extraction and a bidirectional Gated Recurrent Unit for capturing inter-slice spatial dependencies.
• Score: 3.093890460224435
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Glaucoma, a leading cause of irreversible blindness, necessitates early detection for accurate and timely intervention to prevent irreversible vision loss. In this study, we present a novel deep learning framework that leverages the diagnostic value of 3D Optical Coherence Tomography (OCT) imaging for automated glaucoma detection. In this framework, we integrate a pre-trained Vision Transformer on retinal data for rich slice-wise feature extraction and a bidirectional Gated Recurrent Unit for capturing inter-slice spatial dependencies. This dual-component approach enables comprehensive analysis of local nuances and global structural integrity, crucial for accurate glaucoma diagnosis. Experimental results on a large dataset demonstrate the superior performance of the proposed method over state-of-the-art ones, achieving an F1-score of 93.01%, Matthews Correlation Coefficient (MCC) of 69.33%, and AUC of 94.20%. The framework's ability to leverage the valuable information in 3D OCT data holds significant potential for enhancing clinical decision support systems and improving patient outcomes in glaucoma management.

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