Title: Automatic detection of novel corona virus (SARS-CoV-2) infection in computed tomography scan based on local adaptive thresholding and kernel-support vectors

Authors: Ritam Sharma; Janki Ballabh Sharma; Ranjan Maheshwari

Addresses: Department of Electronics Engineering, Rajasthan Technical University, Kota, India ' Department of Electronics Engineering, Rajasthan Technical University, Kota, India ' Department of Electronics Engineering, Rajasthan Technical University, Kota, India

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.

Keywords: SARS-CoV-2; COVID-19; computed tomography; CT; artificial intelligence; textural feature; adaptive thresholding; support vector machine; SVM.

DOI: 10.1504/IJMEI.2023.129348

International Journal of Medical Engineering and Informatics, 2023 Vol.15 No.2, pp.139 - 152

Received: 03 Oct 2020
Accepted: 28 Feb 2021
Published online: 07 Mar 2023 *

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