Computer-Aided Detection of COVID-19 using Chest Imaging
Computer-Aided Detection of COVID-19 using Chest Imaging
- Author(s): Sahar Liaqat 1 and Gulistan Raja 2
- DOI: 10.1049/icp.2021.1458
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- Author(s): Sahar Liaqat 1 and Gulistan Raja 2
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View affiliations
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Affiliations:
1:
University of Engineering and Technology , Taxila , Pakistan ;
2: University of Engineering and Technology , Taxila , Pakistan
Source:
11th International Conference of Pattern Recognition Systems (ICPRS 2021),
2021
p.
133 – 137
-
Affiliations:
1:
University of Engineering and Technology , Taxila , Pakistan ;
- Conference: 11th International Conference of Pattern Recognition Systems (ICPRS 2021)
- DOI: 10.1049/icp.2021.1458
- ISBN: 978-1-83953-430-0
- Location: Online Conference
- Conference date: 17-19 March 2021
- Format: PDF
Coronavirus disease 2019 (COVID-19), a microbial infection, is induced by Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2). Automatic computer-aided detection can help detect the infected patients using chest imaging i.e., CXR and CT scans. This paper proposes a method to detect infected patients with high accuracy using chest X-ray image of a patient. It proposes a model that includes deep model features and Local Binary Pattern (LBP) features along with Support Vector Machine (SVM) classifier. The model is utilized for binary classification among normal and COVID-19 infected patients X-rays. Proposed model produced 99.4% accuracy using VGG16 deep features along with LBP features and the Gaussian SVM classifier.
Inspec keywords: image texture; diseases; diagnostic radiography; image classification; patient diagnosis; support vector machines; pattern classification; feature extraction; molecular biophysics; computerised tomography; microorganisms; medical image processing
Subjects: Optical and laser radiation (medical uses); X-rays and particle beams (medical uses); Biology and medical computing; Computer vision and image processing techniques; X-ray techniques: radiography and computed tomography (biomedical imaging/measurement); Patient diagnostic methods and instrumentation; Other topics in statistics; Optical, image and video signal processing; Biomedical measurement and imaging; Biomedical engineering; Optical and laser radiation (biomedical imaging/measurement); Other topics in statistics
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