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Using a Deep Learning Model to Explore the Impact of Clinical Data on COVID-19 Diagnosis Using Chest X-ray.
Khan, Irfan Ullah; Aslam, Nida; Anwar, Talha; Alsaif, Hind S; Chrouf, Sara Mhd Bachar; Alzahrani, Norah A; Alamoudi, Fatimah Ahmed; Kamaleldin, Mariam Moataz Aly; Awary, Khaled Bassam.
Afiliación
  • Khan IU; Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia.
  • Aslam N; Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia.
  • Anwar T; School of Computing, National University of Computer and Emerging Sciences, Islamabad 44000, Pakistan.
  • Alsaif HS; Radiology Department, King Fahd Hospital of the University, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia.
  • Chrouf SMB; Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia.
  • Alzahrani NA; Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia.
  • Alamoudi FA; National Center for Artificial Intelligence (NCAI), Saudi Data and Artificial Intelligence Authority (SDAIA), Riyadh 12391, Saudi Arabia.
  • Kamaleldin MMA; Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia.
  • Awary KB; Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia.
Sensors (Basel) ; 22(2)2022 Jan 16.
Article en En | MEDLINE | ID: mdl-35062629
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Aprendizaje Profundo / COVID-19 Tipo de estudio: Diagnostic_studies / Prognostic_studies Límite: Humans Idioma: En Revista: Sensors (Basel) Año: 2022 Tipo del documento: Article País de afiliación: Arabia Saudita Pais de publicación: Suiza
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Aprendizaje Profundo / COVID-19 Tipo de estudio: Diagnostic_studies / Prognostic_studies Límite: Humans Idioma: En Revista: Sensors (Basel) Año: 2022 Tipo del documento: Article País de afiliación: Arabia Saudita Pais de publicación: Suiza