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

TL;DR

This paper introduces a novel deep learning framework combining Vision Transformer and GRU to analyze 3D OCT images for improved glaucoma diagnosis, demonstrating superior performance over existing methods.

Contribution

It presents a new spatial-aware Transformer-GRU framework that effectively captures local and global features in 3D OCT data for glaucoma detection.

Findings

Achieved an F1-score of 93.01% on a large dataset.

Outperformed state-of-the-art methods in glaucoma detection.

Demonstrated the framework's potential for clinical decision support.

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.