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COVID-index: A texture-based approach to classifying lung lesions based on CT images
COVID-19 is an infectious disease caused by a newly discovered type of coronavirus called SARS-CoV-2. Since the discovery of this disease in late 2019, COVID-19 has become a worldwide concern, mainly due to its high degree of contagion. As of April 2021, the number of confirmed cases of COVID-19 rep...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier Ltd.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8180348/ https://www.ncbi.nlm.nih.gov/pubmed/34121775 http://dx.doi.org/10.1016/j.patcog.2021.108083 |
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author | de Carvalho Brito, Vitória dos Santos, Patrick Ryan Sales de Sales Carvalho, Nonato Rodrigues de Carvalho Filho, Antonio Oseas |
author_facet | de Carvalho Brito, Vitória dos Santos, Patrick Ryan Sales de Sales Carvalho, Nonato Rodrigues de Carvalho Filho, Antonio Oseas |
author_sort | de Carvalho Brito, Vitória |
collection | PubMed |
description | COVID-19 is an infectious disease caused by a newly discovered type of coronavirus called SARS-CoV-2. Since the discovery of this disease in late 2019, COVID-19 has become a worldwide concern, mainly due to its high degree of contagion. As of April 2021, the number of confirmed cases of COVID-19 reported to the World Health Organization has already exceeded 135 million worldwide, while the number of deaths exceeds 2.9 million. Due to the impacts of the disease, efforts in the literature have intensified in terms of studying approaches aiming to detect COVID-19, with a focus on supporting and facilitating the process of disease diagnosis. This work proposes the application of texture descriptors based on phylogenetic relationships between species to characterize segmented CT volumes, and the subsequent classification of regions into COVID-19, solid lesion or healthy tissue. To evaluate our method, we use images from three different datasets. The results are promising, with an accuracy of 99.93%, a recall of 99.93%, a precision of 99.93%, an F1-score of 99.93%, and an AUC of 0.997. We present a robust, simple, and efficient method that can be easily applied to 2D and/or 3D images without limitations on their dimensionality. |
format | Online Article Text |
id | pubmed-8180348 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Elsevier Ltd. |
record_format | MEDLINE/PubMed |
spelling | pubmed-81803482021-06-07 COVID-index: A texture-based approach to classifying lung lesions based on CT images de Carvalho Brito, Vitória dos Santos, Patrick Ryan Sales de Sales Carvalho, Nonato Rodrigues de Carvalho Filho, Antonio Oseas Pattern Recognit Article COVID-19 is an infectious disease caused by a newly discovered type of coronavirus called SARS-CoV-2. Since the discovery of this disease in late 2019, COVID-19 has become a worldwide concern, mainly due to its high degree of contagion. As of April 2021, the number of confirmed cases of COVID-19 reported to the World Health Organization has already exceeded 135 million worldwide, while the number of deaths exceeds 2.9 million. Due to the impacts of the disease, efforts in the literature have intensified in terms of studying approaches aiming to detect COVID-19, with a focus on supporting and facilitating the process of disease diagnosis. This work proposes the application of texture descriptors based on phylogenetic relationships between species to characterize segmented CT volumes, and the subsequent classification of regions into COVID-19, solid lesion or healthy tissue. To evaluate our method, we use images from three different datasets. The results are promising, with an accuracy of 99.93%, a recall of 99.93%, a precision of 99.93%, an F1-score of 99.93%, and an AUC of 0.997. We present a robust, simple, and efficient method that can be easily applied to 2D and/or 3D images without limitations on their dimensionality. Elsevier Ltd. 2021-11 2021-06-06 /pmc/articles/PMC8180348/ /pubmed/34121775 http://dx.doi.org/10.1016/j.patcog.2021.108083 Text en © 2021 Elsevier Ltd. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Article de Carvalho Brito, Vitória dos Santos, Patrick Ryan Sales de Sales Carvalho, Nonato Rodrigues de Carvalho Filho, Antonio Oseas COVID-index: A texture-based approach to classifying lung lesions based on CT images |
title | COVID-index: A texture-based approach to classifying lung lesions based on CT images |
title_full | COVID-index: A texture-based approach to classifying lung lesions based on CT images |
title_fullStr | COVID-index: A texture-based approach to classifying lung lesions based on CT images |
title_full_unstemmed | COVID-index: A texture-based approach to classifying lung lesions based on CT images |
title_short | COVID-index: A texture-based approach to classifying lung lesions based on CT images |
title_sort | covid-index: a texture-based approach to classifying lung lesions based on ct images |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8180348/ https://www.ncbi.nlm.nih.gov/pubmed/34121775 http://dx.doi.org/10.1016/j.patcog.2021.108083 |
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