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An Ensemble Machine Learning Model to Detect COVID-19 Using Chest X-Ray

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Frontiers of ICT in Healthcare

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 519))

Abstract

The COVID-19 which is caused by the severe acute respiratory syndrome coronavirus 2 or SARS-CoV-2, has taken a lot of human life and still continuing, and significantly disrupting the healthcare system. Due to challenges and controversies to testing for COVID-19, improved, alternative cost-effective, and machine learning methods are needed to detect the disease and related data analysis. For this purpose, machine learning (ML) approaches emerge as a strong forecasting method to detect a disease including COVID-19. Our proposed ensemble machine learning (EML) is a technique that leverages multiple deep learning models and then combines them to produce improved results. In this paper, we proposed an EML approach to detect COVID-19 using chest x-ray images. Radiographic images are readily available, which can be used as an effective tool compared to other expensive and time-consuming pathological tests, but not to replace pathological tests but rather give alternative extra confirmation and more detailed analysis to the medical fraternity. In conclusion, automatic computational machine learning models allow for rapid analysis of chest X-ray images and thus enable radiologists to filter potential candidates in a time-effective manner to detect COVID-19. Our proposed approach has very promising results with an average detection accuracy of 93.56% and a sensitivity of 91.24%, and an F1 score is 0.91.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Information Systems, Leonard C. Nelson College of Engineering and Sciences, West Virginia University Institute of Technology, Beckley, USA

    Somenath Chakraborty

  2. Computer Science and Engineering, University of North Texas, Denton, USA

    Beddhu Murali

Authors
  1. Somenath Chakraborty
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  2. Beddhu Murali
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Corresponding author

Correspondence to Somenath Chakraborty .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, University of Kalyani, Kalyani, West Bengal, India

    Jyotsna Kumar Mandal

  2. Department of Computer Science and Engineering, School of Computational Science, Maulana Abul Kalam Azad University of Technology, Kolkata, West Bengal, India

    Debashis De

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Chakraborty, S., Murali, B. (2023). An Ensemble Machine Learning Model to Detect COVID-19 Using Chest X-Ray. In: Mandal, J.K., De, D. (eds) Frontiers of ICT in Healthcare . Lecture Notes in Networks and Systems, vol 519. Springer, Singapore. https://doi.org/10.1007/978-981-19-5191-6_36

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