GS-Net: Global Self-Attention Guided CNN for Multi-Stage Glaucoma Classification

TL;DR

GS-Net introduces a global self-attention mechanism with channel and spatial modules to improve multi-stage glaucoma classification from retinal images, outperforming existing methods.

Contribution

The paper presents a novel global self-attention guided CNN architecture, GS-Net, specifically designed for multi-stage glaucoma detection, addressing limitations of binary classification approaches.

Findings

GS-Net outperforms state-of-the-art methods on a public dataset.

GSAM achieves competitive performance against other attention modules.

The approach effectively captures global feature dependencies for better classification.

Abstract

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.