Spatial-aware Transformer-GRU Framework for Enhanced Glaucoma Diagnosis
  from 3D OCT Imaging
Mona Ashtari-Majlan Mohammad Mahdi Dehshibi David Masip
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%, Matthews Correlation Coefficient (MCC) of 73.54%, and AUC
of 95.24%. 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.