Deep learning framework for rapid and accurate respiratory COVID-19 prediction using chest X-ray images.

Ukwuoma, Chiagoziem C; Cai, Dongsheng; Heyat, Md Belal Bin; Bamisile, Olusola; Adun, Humphrey; Al-Huda, Zaid; Al-Antari, Mugahed A

Ukwuoma, Chiagoziem C; Cai, Dongsheng; Heyat, Md Belal Bin; Bamisile, Olusola; Adun, Humphrey; Al-Huda, Zaid; Al-Antari, Mugahed A.

Afiliación

Ukwuoma CC; The College of Nuclear Technology and Automation Engineering, Chengdu University of Technology, Sichuan, 610059, China.
Cai D; The College of Nuclear Technology and Automation Engineering, Chengdu University of Technology, Sichuan, 610059, China.
Heyat MBB; IoT Research Center, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong 518060, China.
Bamisile O; Sichuan Industrial Internet Intelligent Monitoring and Application Engineering Technology Research Center, Chengdu University of Technology, China.
Adun H; Department of Mechanical and Energy Systems Engineering, Cyprus International University, Nicosia, North Nicosia, Cyprus.
Al-Huda Z; School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, Sichuan, China.
Al-Antari MA; Department of Artificial Intelligence, College of Software & Convergence Technology, Daeyang AI Center, Sejong University, Seoul 05006, Republic of Korea.

J King Saud Univ Comput Inf Sci ; 35(7): 101596, 2023 Jul.

Article en En | MEDLINE | ID: mdl-37275558

Palabras clave

COVID-19 prediction; Explainable Artificial Intelligence (XAI); Hybrid ensemble deep feature extraction; Multi-head self-attention network; Visual explainable saliency maps

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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: J King Saud Univ Comput Inf Sci Año: 2023 Tipo del documento: Article País de afiliación: China

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