An integrated autoencoder-based hybrid CNN-LSTM model for COVID-19 severity prediction from lung ultrasound

The COVID-19 pandemic has become one of the biggest threats to the global healthcare system, creating an unprecedented condition worldwide. The necessity of rapid diagnosis calls for alternative methods to predict the condition of the patient, for which disease severity estimation on the basis of Lu...

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Vydáno v:	Computers in biology and medicine Ročník 132; s. 104296
Hlavní autoři:	Dastider, Ankan Ghosh, Sadik, Farhan, Fattah, Shaikh Anowarul
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States Elsevier Ltd 01.05.2021 Elsevier Limited Published by Elsevier Ltd
Témata:	Artificial neural networks Classification Computer architecture Coronaviruses COVID-19 Datasets Deep learning Disease transmission Internal Medicine Long short-term memory Lung ultrasound Lungs Machine learning Medical imaging Methods Neural network Neural networks Other Pandemics Recurrent neural networks Severity score Temporal variations Ultrasonic imaging Ultrasound X-rays COVID-19 Deep learning Severity score Lung ultrasound Neural network
ISSN:	0010-4825, 1879-0534, 1879-0534
On-line přístup:	Získat plný text
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