Automatic detection of novel corona virus (SARS-CoV-2) infection in computed tomography scan based on local adaptive thresholding and kernel-support vectors
International Journal of Medical Engineering and Informatics
; 15(2):139-152, 2022.
Article
in English
| EMBASE | ID: covidwho-2319213
ABSTRACT
The recent studies have indicated the requisite of computed tomography scan analysis by radiologists extensively to find out the suspected patients of SARS-CoV-2 (COVID-19). The existing deep learning methods distribute one or more of the subsequent bottlenecks. Therefore, a straight forward method for detecting COVID-19 infection using real-world computed tomography scans is presented. The detection process consists of image processing techniques such as segmentation of lung parenchyma and extraction of effective texture features. The kernel-based support vector machine is employed over feature vectors for classification. The performance parameters of the proposed method are calculated and compared with the existing methodology on the same dataset. The classification results are found outperforming and the method is less probabilistic which can be further exploited for developing more realistic detection system.Copyright © 2023 Inderscience Enterprises Ltd.
adaptive thresholding; artificial intelligence; computed tomography; covid-19; ct; SARS-CoV-2; support vector machine; svm; textural feature; article; computer assisted tomography; computer model; cone beam computed tomography; controlled study; coronavirus disease 2019; cycle threshold value; diagnostic accuracy; diagnostic test accuracy study; extraction; human; image processing; image segmentation; kernel method; learning algorithm; lung parenchyma; machine learning; mathematical model; nuclear magnetic resonance imaging; predictive value; principal component analysis; receiver operating characteristic; sensitivity and specificity; Severe acute respiratory syndrome coronavirus 2; walking; X ray diffraction
Full text:
Available
Collection:
Databases of international organizations
Database:
EMBASE
Language:
English
Journal:
International Journal of Medical Engineering and Informatics
Year:
2022
Document Type:
Article
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