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Diagnosing COVID-19 From Chest CT Scan Images Using Deep Learning Models

Diagnosing COVID-19 From Chest CT Scan Images Using Deep Learning Models

Shamik Tiwari, Anurag Jain, Sunil Kumar Chawla
Copyright: © 2022 |Volume: 11 |Issue: 2 |Pages: 15
ISSN: 2160-9551|EISSN: 2160-956X|EISBN13: 9781683182580|DOI: 10.4018/IJRQEH.299961
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MLA

Tiwari, Shamik, et al. "Diagnosing COVID-19 From Chest CT Scan Images Using Deep Learning Models." IJRQEH vol.11, no.2 2022: pp.1-15. http://doi.org/10.4018/IJRQEH.299961

APA

Tiwari, S., Jain, A., & Chawla, S. K. (2022). Diagnosing COVID-19 From Chest CT Scan Images Using Deep Learning Models. International Journal of Reliable and Quality E-Healthcare (IJRQEH), 11(2), 1-15. http://doi.org/10.4018/IJRQEH.299961

Chicago

Tiwari, Shamik, Anurag Jain, and Sunil Kumar Chawla. "Diagnosing COVID-19 From Chest CT Scan Images Using Deep Learning Models," International Journal of Reliable and Quality E-Healthcare (IJRQEH) 11, no.2: 1-15. http://doi.org/10.4018/IJRQEH.299961

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Abstract

A novel coronavirus named COVID-19 has spread speedily and has triggered a worldwide outbreak of respiratory illness. Early diagnosis is always crucial for pandemic control. Compared to RT-PCR, chest computed tomography (CT) imaging is the more consistent, concrete, and prompt method to identify COVID-19 patients. For clinical diagnostics, the information received from computed tomography scans is critical. So there is a need to develop an image analysis technique for detecting viral epidemics from computed tomography scan pictures. Using DenseNet, ResNet, CapsNet, and 3D-ConvNet, four deep machine learning-based architectures have been proposed for COVID-19 diagnosis from chest computed tomography scans. From the experimental results, it is found that all the architectures are providing effective accuracy, of which the COVID-DNet model has reached the highest accuracy of 99%. Proposed architectures are accessible at https://github.com/shamiktiwari/CTscanCovi19 can be utilized to support radiologists and reserachers in validating their initial screening.