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A Review of Deep Learning-Based Methods for the Diagnosis and Prediction of COVID-19

A Review of Deep Learning-Based Methods for the Diagnosis and Prediction of COVID-19

Jiaji Wang
Copyright: © 2022 |Volume: 12 |Issue: 1 |Pages: 17
ISSN: 2641-6255|EISSN: 2641-6263|EISBN13: 9781683183693|DOI: 10.4018/IJPCH.311444
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MLA

Wang, Jiaji. "A Review of Deep Learning-Based Methods for the Diagnosis and Prediction of COVID-19." IJPCH vol.12, no.1 2022: pp.1-17. http://doi.org/10.4018/IJPCH.311444

APA

Wang, J. (2022). A Review of Deep Learning-Based Methods for the Diagnosis and Prediction of COVID-19. International Journal of Patient-Centered Healthcare (IJPCH), 12(1), 1-17. http://doi.org/10.4018/IJPCH.311444

Chicago

Wang, Jiaji. "A Review of Deep Learning-Based Methods for the Diagnosis and Prediction of COVID-19," International Journal of Patient-Centered Healthcare (IJPCH) 12, no.1: 1-17. http://doi.org/10.4018/IJPCH.311444

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Abstract

In 2019, the outbreak of a new coronavirus spread rapidly around the world. The use of medical image-assisted diagnosis for suspected patients can provide a more accurate and rapid picture of the disease. The earlier the diagnosis is made and the earlier the patient is treated, the lower the likelihood of virus transmission. This paper reviews current research advances in the processing of lung CT images in combination with promising deep learning, including image segmentation, recognition, and classification, and provides a comparison in a tabular format, hoping to provide inspiration for their future development.

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