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.
Garea-Llano, E, Diaz-Berenguer, A, Sahli, H & Gonzalez-Dalmau, E 2023, Chest X-Ray Imaging Severity Score of COVID-19 Pneumonia. in AY RodrĂguez-González, H PĂ©rez-Espinosa, JF MartĂnez-Trinidad, JA Carrasco-Ochoa & JA Olvera-LĂłpez (eds), Lecture Notes in Computer Science: Mexican Conference on Pattern Recognition. vol. 13902, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13902 LNCS, Springer, Cham, pp. 211–220. https://doi.org/10.1007/978-3-031-33783-3_20
Garea-Llano, E., Diaz-Berenguer, A., Sahli, H., & Gonzalez-Dalmau, E. (2023). Chest X-Ray Imaging Severity Score of COVID-19 Pneumonia. In A. Y. RodrĂguez-González, H. PĂ©rez-Espinosa, J. F. MartĂnez-Trinidad, J. A. Carrasco-Ochoa, & J. A. Olvera-LĂłpez (Eds.), Lecture Notes in Computer Science: Mexican Conference on Pattern Recognition (Vol. 13902, pp. 211–220). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13902 LNCS). Springer, Cham. https://doi.org/10.1007/978-3-031-33783-3_20
@inbook{8febd45831cf4594a4485f17f74af098,
title = "Chest X-Ray Imaging Severity Score of COVID-19 Pneumonia",
abstract = "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.",
author = "Eduardo Garea-Llano and Abel Diaz-Berenguer and Hichem Sahli and Evelio Gonzalez-Dalmau",
note = "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: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. Copyright: Copyright 2023 Elsevier B.V., All rights reserved.",
year = "2023",
month = jun,
day = "9",
doi = "10.1007/978-3-031-33783-3_20",
language = "English",
isbn = "978-3-031-33782-6",
volume = "13902",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer, Cham",
pages = "211–220",
editor = "Rodr{\'i}guez-Gonz{\'a}lez, {Ansel Yoan} and Humberto P{\'e}rez-Espinosa and Mart{\'i}nez-Trinidad, {Jos{\'e} Francisco} and Carrasco-Ochoa, {Jes{\'u}s Ariel} and Olvera-L{\'o}pez, {Jos{\'e} Arturo}",
booktitle = "Lecture Notes in Computer Science",
}