Spatial-aware Transformer-GRU Framework for Enhanced Glaucoma Diagnosis from 3D OCT Imaging
<|reference_start|>Spatial-aware Transformer-GRU Framework for Enhanced Glaucoma Diagnosis from 3D OCT Imaging: 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.<|reference_end|>
@article{ashtari-majlan2024spatial-aware,
    title={Spatial-aware Transformer-GRU Framework for Enhanced Glaucoma Diagnosis
  from 3D OCT Imaging},
    author={Mona Ashtari-Majlan, Mohammad Mahdi Dehshibi, David Masip},
    journal={arXiv preprint arXiv:2403.05702},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.05702},
    primaryClass={eess.IV cs.CV}
}