A novel CT image de-noising and fusion based deep learning network to screen for disease (COVID-19).

Khan, Sajid Ullah; Ullah, Imdad; Ullah, Najeeb; Shah, Sajid; Affendi, Mohammed El; Lee, Bumshik

Khan, Sajid Ullah; Ullah, Imdad; Ullah, Najeeb; Shah, Sajid; Affendi, Mohammed El; Lee, Bumshik.

Afiliação

Khan SU; Multimedia Information Processing Lab, Department of Information and Communication Engineering, Chosun University, Gwangju, South Korea.
Ullah I; Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia.
Ullah N; Department of Computer Science, University of Engineering &Technology, Mardan, KPK, Pakistan.
Shah S; EIAS Data Science Lab, College of Computer and Information Sciences, Prince Sultan University, Riyadh, 11586, Saudi Arabia.
Affendi ME; EIAS Data Science Lab, College of Computer and Information Sciences, Prince Sultan University, Riyadh, 11586, Saudi Arabia.
Lee B; Multimedia Information Processing Lab, Department of Information and Communication Engineering, Chosun University, Gwangju, South Korea. sajdi786@yahoo.com.

Sci Rep ; 13(1): 6601, 2023 04 23.

Article em En | MEDLINE | ID: mdl-37088788

Assuntos

COVID-19; Aprendizado Profundo; Humanos; COVID-19/diagnóstico por imagem; SARS-CoV-2; Teste para COVID-19; Tomografia Computadorizada por Raios X/métodos

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Aprendizado Profundo / COVID-19 Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Revista: Sci Rep Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Coréia do Sul País de publicação: Reino Unido

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