Early detection of silent hypoxia in COVID-19 pneumonia using deep learning and IoT

Unlike normal pneumonia, in COVID-19 pneumonia, the shortfall of oxygen occurs without any noticeable breathing difficulties leads to multiple organ failure and death. The early detection of silent hypoxia in COVID-19 pneumonia is the key to save many lives from this deadly disease. This paper has p...

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Veröffentlicht in:	Multimedia tools and applications Jg. 83; H. 8; S. 24527 - 24539
Hauptverfasser:	Bandopadhaya, Shuvabrata, Roy, Amarjit
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.03.2024 Springer Nature B.V
Schlagworte:	Computer Communication Networks Computer Science COVID-19 Data collection Data Structures and Information Theory Deep learning Hypoxia Internet of Things Mathematical models Multimedia Information Systems Oxygen Oxygen content Pneumonia Special Purpose and Application-Based Systems Track 2: Medical Applications of Multimedia Deep learning Internet of things Silent hypoxia COVID-19 pneumonia LSTM encoder-decoder model Time-series forecasting
ISSN:	1573-7721, 1380-7501, 1573-7721
Online-Zugang:	Volltext
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