Article: Gradient and statistical features-based prediction system for COVID-19 using chest X-ray images Journal: International Journal of Computer Applications in Technology (IJCAT) 2021 Vol.66 No.3/4 pp.362 - 373 Abstract: As per data available on WHO website, COVID-19 patients on 20 June 2020 have surpassed the figure of 8.7 million globally and around 4.6 lakhs have lost their life. The most common diagnostic test for COVID-19 detection is a Polymerase Chain Reaction (PCR) test. In highly populated developing countries like Brazil, India etc., there has been a severe shortage of PCR test-kits. Furthermore, the PCR-test is very specific and has lower sensitivity. In this research work, authors have designed a decision support system based on statistical features and edge maps of X-ray images to detect COVID-19 virus in a patient. Online available data sets of chest X-ray images have been used to train and test decision tree, K-nearest neighbour's, random forest, and multilayer perceptron machine learning classifiers. From the experimental results, it has found that the multilayer perceptron achieved 94% accuracy which is higher than the other classifiers. Inderscience Publishers - linking academia, business and industry through research

Title: Gradient and statistical features-based prediction system for COVID-19 using chest X-ray images

Authors: Anurag Jain; Shamik Tiwari; Tanupriya Choudhury; Bhupesh Kumar Dewangan

Addresses: School of Computer Sciences, University of Petroleum and Energy Studies, Dehradun, Uttarakhand, India ' School of Computer Sciences, University of Petroleum and Energy Studies, Dehradun, Uttarakhand, India ' Department of Informatics, School of Computer Science, University of Petroleum and Energy Studies, Dehradun, Uttarakhand, India ' Department of Informatics, School of Computer Science, University of Petroleum and Energy Studies, Dehradun, Uttarakhand, India

Abstract: As per data available on WHO website, COVID-19 patients on 20 June 2020 have surpassed the figure of 8.7 million globally and around 4.6 lakhs have lost their life. The most common diagnostic test for COVID-19 detection is a Polymerase Chain Reaction (PCR) test. In highly populated developing countries like Brazil, India etc., there has been a severe shortage of PCR test-kits. Furthermore, the PCR-test is very specific and has lower sensitivity. In this research work, authors have designed a decision support system based on statistical features and edge maps of X-ray images to detect COVID-19 virus in a patient. Online available data sets of chest X-ray images have been used to train and test decision tree, K-nearest neighbour's, random forest, and multilayer perceptron machine learning classifiers. From the experimental results, it has found that the multilayer perceptron achieved 94% accuracy which is higher than the other classifiers.

Keywords: COVID-19; chest X-ray; statistical features; image gradient; random forest; KNN; multilayer perceptron; decision tree.

DOI: 10.1504/IJCAT.2021.120464

International Journal of Computer Applications in Technology, 2021 Vol.66 No.3/4, pp.362 - 373

Received: 01 Aug 2020
Accepted: 04 Nov 2020
Published online: 21 Jan 2022 *

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Title: Gradient and statistical features-based prediction system for COVID-19 using chest X-ray images

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