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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.
Afiliação
  • de Carvalho Brito V; Department of Information Systems, Federal University of Piauí R. Cícero Duarte, 905, Junco, Picos 64607-670, PI, Brazil.
  • Dos Santos PRS; Department of Electrical Engineering, Federal University of Piauí - PI, Teresina, Brazil.
  • de Sales Carvalho NR; Department of Information Systems, Federal University of Piauí R. Cícero Duarte, 905, Junco, Picos 64607-670, PI, Brazil.
  • de Carvalho Filho AO; Department of Electrical Engineering, Federal University of Piauí - PI, Teresina, Brazil.
Pattern Recognit ; 119: 108083, 2021 Nov.
Article em En | MEDLINE | ID: mdl-34121775
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.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Pattern Recognit Ano de publicação: 2021 Tipo de documento: Article País de afiliação: Brasil País de publicação: Reino Unido

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Pattern Recognit Ano de publicação: 2021 Tipo de documento: Article País de afiliação: Brasil País de publicação: Reino Unido