COVID-Nets: deep CNN architectures for detecting COVID-19 using chest CT scans

In this paper we propose two novel deep convolutional network architectures, CovidResNet and CovidDenseNet, to diagnose COVID-19 based on CT images. The models enable transfer learning between different architectures, which might significantly boost the diagnostic performance. Whereas novel architec...

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Veröffentlicht in:	PeerJ. Computer science Jg. 7; S. e655
Hauptverfasser:	Alshazly, Hammam, Linse, Christoph, Abdalla, Mohamed, Barth, Erhardt, Martinetz, Thomas
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States PeerJ. Ltd 29.07.2021 PeerJ, Inc PeerJ Inc
Schlagworte:	Artificial Intelligence Automated diagnosis Automation Bioinformatics Cable television broadcasting industry Classification Computed tomography Computer architecture Computer Vision Coronaviruses COVID-19 detection CT imaging Data Mining and Machine Learning Datasets Deep learning Health aspects Medical imaging Medical imaging equipment Multi-class classification Pneumonia SARS-CoV-2 Sensitivity Viral infections United States Taiwan Deep learning Multi-class classification SARS-CoV-2 COVID-19 detection Computed tomography Automated diagnosis
ISSN:	2376-5992, 2376-5992
Online-Zugang:	Volltext
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