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

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原文链接

NETL

外文摘要：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。

外文关键词：

Chest X-rayCOVID-19SeverityClassification

作者：

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

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作者单位：

Cuban Neuroscience Center, 190 No. 1520, 11600 Playa, Havana, Cuba

Faculty of Engineering Sciences, Department of Electronics and Informatics, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussel, Belgium

Faculty of Engineering Sciences, Department of Electronics and Informatics, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussel, Belgium, Interuniversity Microelectronics Centre (IMEC), Kapeldreef 75, 3001 Haverlee, Belgium

会议名称：

Mexican Conference on Pattern Recognition

会议地点：

Tepic(MX)

会议母体文献：

Pattern Recognition

页码：

211-220

出版时间：

2023

DOI：

10.1007/978-3-031-33783-3_20