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A Hybrid Convolutional Neural Network Model for Diagnosis of COVID-19 Using Chest X-ray Images

Author

Listed:
  • Prabhjot Kaur

    (Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, Punjab, India)

  • Shilpi Harnal

    (Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, Punjab, India)

  • Rajeev Tiwari

    (Department of Systemics, School of Computer Science, University of Petroleum and Energy Studies, Dehradun 248007, Uttarakhand, India)

  • Fahd S. Alharithi

    (Department of Computer Science, College of Computers and Information Technology, Taif University, Taif 21944, Saudi Arabia)

  • Ahmed H. Almulihi

    (Department of Computer Science, College of Computers and Information Technology, Taif University, Taif 21944, Saudi Arabia)

  • Irene Delgado Noya

    (Higher Polytechnic School/Industrial Organization Engineering, Universidad Europea del Atlántico, 39011 Santander, Spain
    Department of Project Management, Universidad Internacional Iberoamericana, Campeche 24560, Mexico)

  • Nitin Goyal

    (Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, Punjab, India)

Abstract

COVID-19 declared as a pandemic that has a faster rate of infection and has impacted the lives and the country’s economy due to forced lockdowns. Its detection using RT-PCR is required long time and due to which its infection has grown exponentially. This creates havoc for the shortage of testing kits in many countries. This work has proposed a new image processing-based technique for the health care systems named “C19D-Net”, to detect “COVID-19” infection from “Chest X-Ray” (XR) images, which can help radiologists to improve their accuracy of detection COVID-19. The proposed system extracts deep learning (DL) features by applying the InceptionV4 architecture and Multiclass SVM classifier to classify and detect COVID-19 infection into four different classes. The dataset of 1900 Chest XR images has been collected from two publicly accessible databases. Images are pre-processed with proper scaling and regular feeding to the proposed model for accuracy attainments. Extensive tests are conducted with the proposed model (“C19D-Net”) and it has succeeded to achieve the highest COVID-19 detection accuracy as 96.24% for 4-classes, 95.51% for three-classes, and 98.1% for two-classes. The proposed method has outperformed well in expressions of “precision”, “accuracy”, “F1-score” and “recall” in comparison with most of the recent previously published methods. As a result, for the present situation of COVID-19, the proposed “C19D-Net” can be employed in places where test kits are in short supply, to help the radiologists to improve their accuracy of detection of COVID-19 patients through XR-Images.

Suggested Citation

  • Prabhjot Kaur & Shilpi Harnal & Rajeev Tiwari & Fahd S. Alharithi & Ahmed H. Almulihi & Irene Delgado Noya & Nitin Goyal, 2021. "A Hybrid Convolutional Neural Network Model for Diagnosis of COVID-19 Using Chest X-ray Images," IJERPH, MDPI, vol. 18(22), pages 1-17, November.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:22:p:12191-:d:683984
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    References listed on IDEAS

    as
    1. Panwar, Harsh & Gupta, P.K. & Siddiqui, Mohammad Khubeb & Morales-Menendez, Ruben & Singh, Vaishnavi, 2020. "Application of deep learning for fast detection of COVID-19 in X-Rays using nCOVnet," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    2. 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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