A novel lightweight deep learning approach for simultaneous optic cup and optic disc segmentation in glaucoma detection

Yantao Song; Wenjie Zhang; Yue Zhang; Yantao Song; Wenjie Zhang; Yue Zhang

doi:10.3934/mbe.2024225

Mathematical Biosciences and Engineering

2024, Volume 21, Issue 4: 5092-5117. doi: 10.3934/mbe.2024225

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A novel lightweight deep learning approach for simultaneous optic cup and optic disc segmentation in glaucoma detection

1.
Institute of Big Data Science and Industry, Shanxi University, Taiyuan 030006, China
2.
School of Computer and Information Technology, Shanxi University, Taiyuan 030006, China

Academic Editor: Namhyuk Ahn

Received: 06 November 2023 Revised: 16 February 2024 Accepted: 26 February 2024 Published: 04 March 2024

Glaucoma is a chronic neurodegenerative disease that can result in irreversible vision loss if not treated in its early stages. The cup-to-disc ratio is a key criterion for glaucoma screening and diagnosis, and it is determined by dividing the area of the optic cup (OC) by that of the optic disc (OD) in fundus images. Consequently, the automatic and accurate segmentation of the OC and OD is a pivotal step in glaucoma detection. In recent years, numerous methods have resulted in great success on this task. However, most existing methods either have unsatisfactory segmentation accuracy or high time costs. In this paper, we propose a lightweight deep-learning architecture for the simultaneous segmentation of the OC and OD, where we have adopted fuzzy learning and a multi-layer perceptron to simplify the learning complexity and improve segmentation accuracy. Experimental results demonstrate the superiority of our proposed method as compared to most state-of-the-art approaches in terms of both training time and segmentation accuracy.
- fuzzy learning,
- multi-layer perceptron,
- neural networks optic disc segmentation,
- optic cup segmentation,
- glaucoma screening
Citation: Yantao Song, Wenjie Zhang, Yue Zhang. A novel lightweight deep learning approach for simultaneous optic cup and optic disc segmentation in glaucoma detection[J]. Mathematical Biosciences and Engineering, 2024, 21(4): 5092-5117. doi: 10.3934/mbe.2024225

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Abstract

Glaucoma is a chronic neurodegenerative disease that can result in irreversible vision loss if not treated in its early stages. The cup-to-disc ratio is a key criterion for glaucoma screening and diagnosis, and it is determined by dividing the area of the optic cup (OC) by that of the optic disc (OD) in fundus images. Consequently, the automatic and accurate segmentation of the OC and OD is a pivotal step in glaucoma detection. In recent years, numerous methods have resulted in great success on this task. However, most existing methods either have unsatisfactory segmentation accuracy or high time costs. In this paper, we propose a lightweight deep-learning architecture for the simultaneous segmentation of the OC and OD, where we have adopted fuzzy learning and a multi-layer perceptron to simplify the learning complexity and improve segmentation accuracy. Experimental results demonstrate the superiority of our proposed method as compared to most state-of-the-art approaches in terms of both training time and segmentation accuracy.

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