Diagnosing COVID-19 From Chest CT Scan Images Using Deep Learning Models
International Journal of Reliable and Quality E - Healthcare
; 11(2):1-15, 2022.
Article
in English
| ProQuest Central | ID: covidwho-1934334
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.
Full text:
Available
Collection:
Databases of international organizations
Database:
ProQuest Central
Language:
English
Journal:
International Journal of Reliable and Quality E - Healthcare
Year:
2022
Document Type:
Article
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