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Automatic Features Extraction from the Optic Cup and Disc Segmentation for Glaucoma Classification

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Computational Science and Its Applications – ICCSA 2023 (ICCSA 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13956 ))

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Abstract

Glaucoma is a disease that progressively affects the optic nerve, the leading cause of blindness worldwide. One of the most assertive strategies to make the diagnosis is Optical Coherence Tomography (OCT) which identifies anomalies in the anatomy of the optic nerve. OCT is a high-cost exam, so some works in the literature have been using computationally expensive deep neural networks to analyze images on retinal fundus images to diagnose glaucoma. As an alternative to these approaches, in this work, we propose a low-cost computational method for extracting characteristics of the optic nerve anatomy (i.e., optic cup and disc segmentation) through the processing of retinal fundus images, which is used in conjunction with lower computational cost classification algorithms (i.e., support vector machine (SVM)), is capable of performing accurate diagnoses. The most dominant attributes were identified using shapely adaptive explanations (SHAP) and local interpretable model-agnostic explanations (LIME) analysis. More specifically, the more precise the extraction of features, the greater the accuracy of the classifier.

This work was partially funded by CNPq, CAPES and Fapemig.

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Authors and Affiliations

  1. Universidade Federal de São João del Rei (UFSJ), São João del Rei, Brazil

    Marcus Oliveira, Cleverson Vieira, Michel Carlo Rodrigues Leles, Diego Dias, Elisa Tuler & Leonardo Rocha

  2. Universidade Federal de São Paulo (UNIFESP), São Paulo, Brazil

    Marcelo Guimarães

  3. Centro de Estudos e Pesquisas Oculistas Associados (CEPOA), Rio de Janeiro, Brazil

    Ana Paula De Filippo

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  1. Marcus Oliveira
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  2. Cleverson Vieira
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  3. Ana Paula De Filippo
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  4. Michel Carlo Rodrigues Leles
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Corresponding author

Correspondence to Diego Dias .

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Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Italy

    Beniamino Murgante

  3. Monash University, Clayton, VIC, Australia

    David Taniar

  4. Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  5. University of Minho, Braga, Portugal

    Ana Cristina Braga

  6. University of Cagliari, Cagliari, Italy

    Chiara Garau

  7. National Technical University of Athens, Athens, Greece

    Anastasia Stratigea

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Oliveira, M. et al. (2023). Automatic Features Extraction from the Optic Cup and Disc Segmentation for Glaucoma Classification. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2023. ICCSA 2023. Lecture Notes in Computer Science, vol 13956 . Springer, Cham. https://doi.org/10.1007/978-3-031-36805-9_36

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  • DOI: https://doi.org/10.1007/978-3-031-36805-9_36

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