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

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 detec...

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Bibliographic Details
Published in2023 IEEE International Conference on Image Processing (ICIP) pp. 3454 - 3458
Main Authors Das, Dipankar, Nayak, Deepak Ranjan
Format Conference Proceeding
LanguageEnglish
Published IEEE 08.10.2023
Subjects
Blindness
CAM
CNN
Feature extraction
Glaucoma
Global self-attention module
Lesions
Multi-stage glaucoma classification
Real-time systems
Retina
SAM
Task analysis
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Summary: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.
DOI:10.1109/ICIP49359.2023.10222689