An integrated autoencoder-based hybrid CNN-LSTM model for COVID-19 severity prediction from lung ultrasound-Reference-Cited by-同舟云学术

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

Published:2021-05 Issue: Volume:132 Page:104296
ISSN:0010-4825
Container-title:Computers in Biology and Medicine
language:en
Short-container-title:Computers in Biology and Medicine

Author:

Dastider Ankan Ghosh^ORCID,Sadik Farhan^ORCID,Fattah Shaikh Anowarul

Publisher

Elsevier BV

Subject

Health Informatics,Computer Science Applications

Reference35 articles.

1. Chest CT for typical coronavirus disease 2019 (COVID-19) pneumonia: relationship to negative RT-PCR testing;Xie;Radiology,2020

2. Correlation of Chest CT and RT-PCR Testing in Coronavirus Disease 2019 (COVID-19) in China: a Report of 1014 Cases;Tao,2020

3. A novel coronavirus outbreak of global health concern;Wang;Lancet,2020

4. Investigation of effective climatology parameters on COVID-19 outbreak in Iran;Ahmadi;Sci. Total Environ.,2020

5. CovidDeep: SARS-CoV-2/COVID-19 Test Based on Wearable Medical Sensors and Efficient Neural Networks;Hassantabar,2020

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3. Enhancing Green Bean Anthracnose Severity Detection via Integrated CNN-LSTM Models;2023 4th International Conference on Intelligent Technologies (CONIT);2024-06-21

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5. A New Auto-Regressive Multi-Variable Modified Auto-Encoder for Multivariate Time-Series Prediction: A Case Study with Application to COVID-19 Pandemics;International Journal of Environmental Research and Public Health;2024-04-18

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