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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Vydané v:Computers in biology and medicine Ročník 132; s. 104296
Hlavní autori: Dastider, Ankan Ghosh, Sadik, Farhan, Fattah, Shaikh Anowarul
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States Elsevier Ltd 01.05.2021
Elsevier Limited
Published by Elsevier Ltd
Predmet:
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
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Popis
Shrnutí: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 Lung Ultrasound (LUS) can be a safe, radiation-free, flexible, and favorable option. In this paper, a frame-based 4-score disease severity prediction architecture is proposed with the integration of deep convolutional and recurrent neural networks to consider both spatial and temporal features of the LUS frames. The proposed convolutional neural network (CNN) architecture implements an autoencoder network and separable convolutional branches fused with a modified DenseNet-201 network to build a vigorous, noise-free classification model. A five-fold cross-validation scheme is performed to affirm the efficacy of the proposed network. In-depth result analysis shows a promising improvement in the classification performance by introducing the Long Short-Term Memory (LSTM) layers after the proposed CNN architecture by an average of 7−12%, which is approximately 17% more than the traditional DenseNet architecture alone. From an extensive analysis, it is found that the proposed end-to-end scheme is very effective in detecting COVID-19 severity scores from LUS images.
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ISSN:0010-4825
1879-0534
1879-0534
DOI:10.1016/j.compbiomed.2021.104296