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An evaluation of lightweight deep learning techniques in medical imaging for high precision COVID-19 diagnostics.

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第一作者： Ogechukwu,Ukwandu

第一单位： Department of Computer, Communication and Information Systems, School of Engineering and Built Environment, Glasgow Caledonian University, UK.;School of Computer Science and Informatics, Cardiff University, Cardiff, Wales.

作者： Ogechukwu,Ukwandu ^[1] ; Hanan,Hindy ^[2] ; Elochukwu,Ukwandu ^[3]

作者单位： Department of Computer, Communication and Information Systems, School of Engineering and Built Environment, Glasgow Caledonian University, UK.;School of Computer Science and Informatics, Cardiff University, Cardiff, Wales. ^[1] Computer Science Department, Faculty of Computer and Information Sciences, Ain Shams University, Cairo 11566, Egypt. ^[2] Department of Applied Computing and Engineering, Cardiff School of Technologies, Cardiff Metropolitan University, Cardiff CF5 2YB, UK. ^[3]

关键词 COVID-19 detection Diagnostic analytics Lightweight deep learning techniques Machine learning Point-of-care Resource-constrained devices

DOI 10.1016/j.health.2022.100096

PMID 37520618

发布时间 2025-06-06

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Healthcare analytics (New York, N.Y.)

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Healthcare analytics (New York, N.Y.)

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An evaluation of lightweight deep learning techniques in medical imaging for high precision COVID-19 diagnostics.

Healthcare analytics (New York, N.Y.)

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An evaluation of lightweight deep learning techniques in medical imaging for high precision COVID-19 diagnostics.

Healthcare analytics (New York, N.Y.)

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