Abstract
Glaucoma is a neurodegenerative, progressive and silent disease that affects the optic nerve, characterized by an increase in intraocular pressure causing irreversible damage to the optic nerve. The difficulty in early diagnosis of glaucoma has posed challenges at the technological and medical level, since it requires not only several years of study, but also experience on the part of medical specialists to examine the images and make a timely diagnosis. During this pandemic of COVID-19 many patients with this pathology have suffered constant changes and intraocular pressure has increased considerably, so early detection is of vital importance, in addition to providing appropriate and timely treatment so that the patient does not lose vision in its entirety and mitigate the effects that COVID-19 has caused in these patients. In this article we present the different techniques for the diagnosis of glaucoma and the automatic detection methods, as well as the analysis of image processing and the results obtained in the preprocessing stage, the characterization of this disease according to different points of view, we also present a thorough analysis of each of the methods proposed for the support of medical diagnosis, the characteristics of each of the classifiers and data of great relevance for future work.
Translated title of the contribution | Métodos para la detección automática de glaucoma y extracción de características mediante diferentes técnicas en la etapa de preprocesamiento en imágenes de fondo de ojo |
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Original language | English (US) |
DOIs | |
State | Published - 31 Jul 2022 |
Event | 13th International Conference on Applied Human Factors and Ergonomics (AHFE 2022) and the Affiliated Conferences - US Duration: 24 Jul 2022 → 28 Jul 2022 https://ahfe.org/ |
Conference
Conference | 13th International Conference on Applied Human Factors and Ergonomics (AHFE 2022) and the Affiliated Conferences |
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Period | 24/07/22 → 28/07/22 |
Internet address |
Keywords
- Fundus images
- Glaucoma detection
- Neural network
- Support of medical diagnosis
CACES Knowledge Areas
- 819A Public Health