Pulmonary lesion subtypes recognition of COVID-19 from radiomics data with three-dimensional texture characterization in computed tomography images.

Li, Wei; Cao, Yangyong; Yu, Kun; Cai, Yibo; Huang, Feng; Yang, Minglei; Xie, Weidong

Li, Wei; Cao, Yangyong; Yu, Kun; Cai, Yibo; Huang, Feng; Yang, Minglei; Xie, Weidong.

Afiliación

Li W; Key Laboratory of Intelligent Computing in Medical Image (MIIC), Northeastern University, Ministry of Education, Shenyang, China.
Cao Y; School of Computer Science and Engineering, Northeastern University, Shenyang, China. 1971582@stu.neu.edu.cn.
Yu K; Biomedical and Information Engineering School, Northeastern University, Shenyang, China.
Cai Y; School of Computer Science and Engineering, Northeastern University, Shenyang, China.
Huang F; Neusoft Medical System Co., Ltd., Shenyang, Liaoning, China.
Yang M; Neusoft Medical System Co., Ltd., Shenyang, Liaoning, China.
Xie W; School of Computer Science and Engineering, Northeastern University, Shenyang, China. 1910638@stu.neu.edu.cn.

Biomed Eng Online ; 20(1): 123, 2021 Dec 05.

Article en En | MEDLINE | ID: mdl-34865622

Asunto(s)

COVID-19; Algoritmos; Humanos; Pulmón; SARS-CoV-2; Tomografía Computarizada por Rayos X

Palabras clave

3D texture feature; COVID-19; Hybrid adaptive feature selection; Lesion subtypes; Radiomics; Random forest

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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: COVID-19 Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: Biomed Eng Online Asunto de la revista: ENGENHARIA BIOMEDICA Año: 2021 Tipo del documento: Article País de afiliación: China

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