Glaucoma is a common eye disease that leads to irreversible blindness unless timely detected. Hence, glaucoma detection at an early stage is of utmost importance for a better treatment plan and ultimately saving the vision. The recent literature has shown the prominence of CNN-based methods to detect glaucoma from retinal fundus images. However, such methods mainly focus on solving binary classification tasks and have not been thoroughly explored for the detection of different glaucoma stages, which is relatively challenging due to minute lesion size variations and high inter-class similarities. This paper proposes a global self-attention based network called GS-Net for efficient multi-stage glaucoma classification. We introduce a global self-attention module (GSAM) consisting of two parallel attention modules, a channel attention module (CAM) and a spatial attention module (SAM), to learn global feature dependencies across channel and spatial dimensions. The GSAM encourages extracting more discriminative and class-specific features from the fundus images. The experimental results on a publicly available dataset demonstrate that our GS-Net outperforms state-of-the-art methods. Also, the GSAM achieves competitive performance against popular attention modules.

本研究针对青光眼早期检测的需求，提出了一种全新的全球自注意力网络GS-Net，旨在解决现有方法在多阶段青光眼分类中的不足。通过引入全球自注意力模块，GS-Net能够更有效地提取眼底图像中的特征，实验结果显示其在分类性能上超越了现有的先进算法，具有显著的临床应用潜力。

GS-Net：全球自注意力引导的卷积神经网络用于多阶段青光眼分类