Spatial-aware Transformer-GRU Framework for Enhanced Glaucoma Diagnosis from 3D OCT Imaging
arxiv(2024)
摘要
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 93.58
of 95.24
OCT data holds significant potential for enhancing clinical decision support
systems and improving patient outcomes in glaucoma management.
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