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Wireless Mobile Communication and Healthcare. 12th EAI International Conference, MobiHealth 2023, Vila Real, Portugal, November 29-30, 2023 Proceedings

Research Article

Glaucoma Grading Using Fundus Images

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BibTeX Plain Text
  • @INPROCEEDINGS{10.1007/978-3-031-60665-6_12,
        author={Mackele Lourrane Jurema da Silva and Marcos Melo Ferreira and Geraldo Braz Junior and Jo\"{a}o Dallyson Sousa de Almeida and Arthur Guilherme Santos Fernandes},
        title={Glaucoma Grading Using Fundus Images},
        proceedings={Wireless Mobile Communication and Healthcare. 12th EAI International Conference, MobiHealth 2023, Vila Real, Portugal, November 29-30, 2023 Proceedings},
        proceedings_a={MOBIHEALTH},
        year={2024},
        month={6},
        keywords={Glaucoma Diagnosis Deep Learning},
        doi={10.1007/978-3-031-60665-6_12}
    }
    
  • Mackele Lourrane Jurema da Silva
    Marcos Melo Ferreira
    Geraldo Braz Junior
    João Dallyson Sousa de Almeida
    Arthur Guilherme Santos Fernandes
    Year: 2024
    Glaucoma Grading Using Fundus Images
    MOBIHEALTH
    Springer
    DOI: 10.1007/978-3-031-60665-6_12
Mackele Lourrane Jurema da Silva1,*, Marcos Melo Ferreira1, Geraldo Braz Junior1, João Dallyson Sousa de Almeida1, Arthur Guilherme Santos Fernandes1
  • 1: Federal University of Maranhao
*Contact email: mackele.ljs@discente.ufma.br

Abstract

Glaucoma is a chronic, progressive eye disease caused by gradual damage to the optic nerve and is considered the major cause of irreversible visual damage. Because it is impossible to reverse the loss of vision caused by the disease, early detection is essential that interventions can be carried out in the early stages of the disease to stop its progression. Fundus imaging is one of the main methods used to diagnose the disease, making it possible to assess the cup-to-disc ratio by a specialist. In this work, we propose a method based on deep learning, which uses fundus images to help detect the disease in its early stages. In this way, the proposed method can have clinical use and be used to develop tools for classifying more serious disease cases. As a best result, the proposed method achieved a kappa value of 0.83.

Keywords
Glaucoma Diagnosis Deep Learning
Published
2024-06-28
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-031-60665-6_12
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