An efficient covid-19 prediction using Penguin Pelican optimization-based recurrent dropout-enabled hybrid deep CNN-BILSTM classifier
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Hardware and Architecture,Media Technology,Software
Link
https://link.springer.com/content/pdf/10.1007/s11042-023-17869-3.pdf
Reference33 articles.
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2. Tolksdorf K, Buda S, Schuler E, Wieler LH, Haas W (2020) Influenza-associated pneumonia as reference to assess seriousness of coronavirus disease (COVID-19). Eurosurveillance 25(11):2000258
3. Rajaraman S, Antani S (2020) Training deep learning algorithms with weakly labeled pneumonia chest X-ray data for COVID-19 detection. MedRxiv (2020).
4. He X, Wang S, Shi S, Chu X, Tang J, Liu X, Yan C, Zhang J and Ding G (2020) Benchmarking deep learning models and automated model design for COVID-19 detection with chest CT scans. MedRxiv (2020): 2020–06.
5. Rajaraman S, Sornapudi S, Alderson PO, Folio LR, Antani SK (2020) Interpreting deep ensemble learning through radiologist annotations for COVID-19 detection in chest radiographs. medRxiv (2020): 2020–07.
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