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Data Information in Online Environments. 4th EAI International Conference, DIONE 2023, Nanchang, China, November 25–27, 2023, Proceedings

Research Article

Deep Learning-Based Glaucoma Diagnostic Assistance System on Mobile Devices

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BibTeX Plain Text
  • @INPROCEEDINGS{10.1007/978-3-031-80713-8_5,
        author={Bin Zhou and Yan Jiang and Ningyi Zhang and Yijian Fu and Yugen Yi and Qiangqiang Zhou},
        title={Deep Learning-Based Glaucoma Diagnostic Assistance System on Mobile Devices},
        proceedings={Data Information in Online Environments. 4th EAI International Conference, DIONE 2023, Nanchang, China, November 25--27, 2023, Proceedings},
        proceedings_a={DIONE},
        year={2025},
        month={2},
        keywords={Glaucoma detection U-net Diagnostic Assistance System},
        doi={10.1007/978-3-031-80713-8_5}
    }
    
  • Bin Zhou
    Yan Jiang
    Ningyi Zhang
    Yijian Fu
    Yugen Yi
    Qiangqiang Zhou
    Year: 2025
    Deep Learning-Based Glaucoma Diagnostic Assistance System on Mobile Devices
    DIONE
    Springer
    DOI: 10.1007/978-3-031-80713-8_5
Bin Zhou1, Yan Jiang1, Ningyi Zhang1, Yijian Fu1, Yugen Yi1, Qiangqiang Zhou1,*
  • 1: School of Software
*Contact email: qiang@jxnu.edu.cn

Abstract

Glaucoma is an irreversible, chronic eye disease for which there is no effective treatment. It is caused by elevated intraocular pressure that damages the optic nerve. There are no symptoms in the early stages of glaucoma, so early diagnosis is crucial to prevent blindness. In this paper, an accurate, reliable, and rapid glaucoma diagnosis method based on computer-aided detection (CAD) technology is presented. The effective use of CAD can significantly reduce the workload and burden of clinical doctors. The CAD system designed in this paper is successfully deployed on the Android platform and provides a user-friendly interface for clinical use. Experimental results demonstrate that the proposed strategy may precisely segment the optic cup and disc in retinal fundus images. Additionally, this research establishes the viability of using deep learning models on mobile devices to partition the optic cup and disc in fundus images.

Keywords
Glaucoma detection U-net Diagnostic Assistance System
Published
2025-02-09
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-031-80713-8_5
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