COVID-19 anomaly detection and classification method based on supervised machine learning of chest X-ray images.

Hasoon, Jamal N; Fadel, Ali Hussein; Hameed, Rasha Subhi; Mostafa, Salama A; Khalaf, Bashar Ahmed; Mohammed, Mazin Abed; Nedoma, Jan

Hasoon, Jamal N; Fadel, Ali Hussein; Hameed, Rasha Subhi; Mostafa, Salama A; Khalaf, Bashar Ahmed; Mohammed, Mazin Abed; Nedoma, Jan.

Afiliación

Hasoon JN; Department of Computer Science, Mustansiriyah University, 10001 Baghdad, Iraq.
Fadel AH; Department of Computer Science, University of Diyala, 32001 Diyala, Iraq.
Hameed RS; Department of Computer Science, University of Diyala, 32001 Diyala, Iraq.
Mostafa SA; Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, 86400 Johor, Malaysia.
Khalaf BA; Department of Medical Instruments Engineering Techniques, Bilad Alrafidain University College, 32001 Diyala, Iraq.
Mohammed MA; College of Computer Science and Information Technology, University of Anbar, Anbar 31001, Iraq.
Nedoma J; Department of Telecommunications, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, 70800 Ostrava, Czech Republic.

Results Phys ; 31: 105045, 2021 Dec.

Article en En | MEDLINE | ID: mdl-34840938

Palabras clave

COVID-19 diagnosis; Haralick; K-nearest neighbor; Local binary pattern; Machine learning; Support vector machine; X-ray image

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Diagnostic_studies / Guideline / Screening_studies Idioma: En Revista: Results Phys Año: 2021 Tipo del documento: Article País de afiliación: Irak Pais de publicación: Países Bajos

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