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Detection of COVID-19 in Chest X-ray Images: A Big Data Enabled Deep Learning Approach

Author

Listed:
  • Mazhar Javed Awan

    (Department of Software Engineering, University of Management and Technology, Lahore 54770, Pakistan)

  • Muhammad Haseeb Bilal

    (Department of Software Engineering, University of Management and Technology, Lahore 54770, Pakistan)

  • Awais Yasin

    (Department of Computer Engineering, National University of Technology, Islamabad 44000, Pakistan)

  • Haitham Nobanee

    (College of Business, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates
    Oxford Centre for Islamic Studies, University of Oxford, Marston Rd, Headington, Oxford OX3 0EE, UK
    Faculty of Humanities & Social Sciences, University of Liverpool, 12 Abercromby Square, Liverpool L69 7WZ, UK)

  • Nabeel Sabir Khan

    (Department of Computer Science, University of Management and Technology, Lahore 54770, Pakistan)

  • Azlan Mohd Zain

    (UTM Big Data Centre, School of Computing, Universiti Teknologi Malaysia, Skudai 81310, Malaysia)

Abstract

Coronavirus disease (COVID-19) spreads from one person to another rapidly. A recently discovered coronavirus causes it. COVID-19 has proven to be challenging to detect and cure at an early stage all over the world. Patients showing symptoms of COVID-19 are resulting in hospitals becoming overcrowded, which is becoming a significant challenge. Deep learning’s contribution to big data medical research has been enormously beneficial, offering new avenues and possibilities for illness diagnosis techniques. To counteract the COVID-19 outbreak, researchers must create a classifier distinguishing between positive and negative corona-positive X-ray pictures. In this paper, the Apache Spark system has been utilized as an extensive data framework and applied a Deep Transfer Learning (DTL) method using Convolutional Neural Network (CNN) three architectures —InceptionV3, ResNet50, and VGG19—on COVID-19 chest X-ray images. The three models are evaluated in two classes, COVID-19 and normal X-ray images, with 100 percent accuracy. But in COVID/Normal/pneumonia, detection accuracy was 97 percent for the inceptionV3 model, 98.55 percent for the ResNet50 Model, and 98.55 percent for the VGG19 model, respectively.

Suggested Citation

  • Mazhar Javed Awan & Muhammad Haseeb Bilal & Awais Yasin & Haitham Nobanee & Nabeel Sabir Khan & Azlan Mohd Zain, 2021. "Detection of COVID-19 in Chest X-ray Images: A Big Data Enabled Deep Learning Approach," IJERPH, MDPI, vol. 18(19), pages 1-16, September.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:19:p:10147-:d:644288
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    References listed on IDEAS

    as
    1. Xue-Jing Liu & Gustavo S. Mesch, 2020. "The Adoption of Preventive Behaviors during the COVID-19 Pandemic in China and Israel," IJERPH, MDPI, vol. 17(19), pages 1-18, September.
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    3. Ouchicha, Chaimae & Ammor, Ouafae & Meknassi, Mohammed, 2020. "CVDNet: A novel deep learning architecture for detection of coronavirus (Covid-19) from chest x-ray images," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    4. Hassantabar, Shayan & Ahmadi, Mohsen & Sharifi, Abbas, 2020. "Diagnosis and detection of infected tissue of COVID-19 patients based on lung x-ray image using convolutional neural network approaches," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    5. Robert Ilijason, 2020. "Beginning Apache Spark Using Azure Databricks," Springer Books, Springer, number 978-1-4842-5781-4, December.
    6. Das, Ayan Kumar & Kalam, Sidra & Kumar, Chiranjeev & Sinha, Ditipriya, 2021. "TLCoV- An automated Covid-19 screening model using Transfer Learning from chest X-ray images," Chaos, Solitons & Fractals, Elsevier, vol. 144(C).
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