Chest X-Ray Imaging Severity Score of COVID-19 Pneumonia

Eduardo Garea-Llano, Abel Diaz-Berenguer, Hichem Sahli, Evelio Gonzalez-Dalmau

Onderzoeksoutput: Chapterpeer review

Samenvatting

Despite the decrease in COVID-19 cases worldwide due to the development of extensive vaccination campaigns and effective containment measures adopted by most countries, this disease continues to be a global concern. Therefore, it is necessary to continue developing methods and algorithms that facilitate decision-making for better treatments. This work proposes a method to evaluate the degree of severity of the affectations caused by COVID-19 in the pulmonary region in chest X-ray images. The proposed algorithm addresses the problem of confusion between pulmonary lesions and anatomical structure (i.e., bones) in chest radiographs. In this paper, we adopt the Semantic Genesis approach for classifying image patches of the lung region into two classes (affected and unaffected). Experiments on a database consisting of X-rays of healthy people and patients with COVID-19 have shown that the proposed approach provides a better assessment of the degree of severity caused by the disease.

Originele taal-2English
TitelLecture Notes in Computer Science
SubtitelMexican Conference on Pattern Recognition
RedacteurenAnsel Yoan Rodríguez-González, Humberto Pérez-Espinosa, José Francisco Martínez-Trinidad, Jesús Ariel Carrasco-Ochoa, José Arturo Olvera-López
UitgeverijSpringer, Cham
Pagina's211–220
Aantal pagina's10
Volume13902
ISBN van elektronische versie978-3-031-33783-3
ISBN van geprinte versie978-3-031-33782-6
DOI's
StatusPublished - 9 jun 2023

Publicatie series

NaamLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13902 LNCS
ISSN van geprinte versie0302-9743
ISSN van elektronische versie1611-3349

Bibliografische nota

Funding Information:
The VLIR-UOS has partially financed this research under the South Initiative: Toward Precision Medicine for the Prediction of Treatment Response to Covid-19 in Cuba (COVID-19 PROMPT).

Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Copyright:
Copyright 2023 Elsevier B.V., All rights reserved.

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