COVID-CheXNet: hybrid deep learning framework for identifying COVID-19 virus in chest X-rays images.

Al-Waisy, Alaa S; Al-Fahdawi, Shumoos; Mohammed, Mazin Abed; Abdulkareem, Karrar Hameed; Mostafa, Salama A; Maashi, Mashael S; Arif, Muhammad; Garcia-Zapirain, Begonya

Al-Waisy, Alaa S; Al-Fahdawi, Shumoos; Mohammed, Mazin Abed; Abdulkareem, Karrar Hameed; Mostafa, Salama A; Maashi, Mashael S; Arif, Muhammad; Garcia-Zapirain, Begonya.

Afiliación

Al-Waisy AS; Communications Engineering Techniques Department, Information Technology Collage, Imam Ja'afar Al-Sadiq University, Baghdad, Iraq.
Al-Fahdawi S; Computer Science Department, Al-Ma'aref University College, Anbar, Iraq.
Mohammed MA; College of Computer Science and Information Technology, University of Anbar, 11, Ramadi, Anbar, Iraq.
Abdulkareem KH; College of Agriculture, Al-Muthanna University, Samawah, 66001 Iraq.
Mostafa SA; Faculty of Computer Science and Information Technology, University Tun Hussein Onn Malaysia, 86400 Batu Pahat, Johor Malaysia.
Maashi MS; Software Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh, 11451 Saudi Arabia.
Arif M; School of Computer Science, Guangzhou University, Guangzhou, China.
Garcia-Zapirain B; eVIDA Lab, The University of Deusto, Avda/Universidades 24, 48007 Bilbao, Spain.

Soft comput ; 27(5): 2657-2672, 2023.

Article en En | MEDLINE | ID: mdl-33250662

Palabras clave

Chest X-ray images; Chest radiography imaging; Coronavirus COVID-19 epidemic; Deep learning; ResNet34 model; Transfer learning

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Soft comput Año: 2023 Tipo del documento: Article País de afiliación: Irak Pais de publicación: Alemania

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