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Novel Comparative Study for the Detection of COVID-19 Using CT Scan and Chest X-ray Images

Author

Listed:
  • Ahatsham Hayat

    (University of Madeira, 9000-082 Funchal, Portugal
    Interactive Technologies Institute (ITI/LARSyS and ARDITI), 9020-105 Funchal, Portugal)

  • Preety Baglat

    (University of Madeira, 9000-082 Funchal, Portugal
    Interactive Technologies Institute (ITI/LARSyS and ARDITI), 9020-105 Funchal, Portugal)

  • Fábio Mendonça

    (University of Madeira, 9000-082 Funchal, Portugal
    Interactive Technologies Institute (ITI/LARSyS and ARDITI), 9020-105 Funchal, Portugal)

  • Sheikh Shanawaz Mostafa

    (Interactive Technologies Institute (ITI/LARSyS and ARDITI), 9020-105 Funchal, Portugal)

  • Fernando Morgado-Dias

    (University of Madeira, 9000-082 Funchal, Portugal
    Interactive Technologies Institute (ITI/LARSyS and ARDITI), 9020-105 Funchal, Portugal)

Abstract

The number of coronavirus disease (COVID-19) cases is constantly rising as the pandemic continues, with new variants constantly emerging. Therefore, to prevent the virus from spreading, coronavirus cases must be diagnosed as soon as possible. The COVID-19 pandemic has had a devastating impact on people’s health and the economy worldwide. For COVID-19 detection, reverse transcription-polymerase chain reaction testing is the benchmark. However, this test takes a long time and necessitates a lot of laboratory resources. A new trend is emerging to address these limitations regarding the use of machine learning and deep learning techniques for automatic analysis, as these can attain high diagnosis results, especially by using medical imaging techniques. However, a key question arises whether a chest computed tomography scan or chest X-ray can be used for COVID-19 detection. A total of 17,599 images were examined in this work to develop the models used to classify the occurrence of COVID-19 infection, while four different classifiers were studied. These are the convolutional neural network (proposed architecture (named, SCovNet) and Resnet18), support vector machine, and logistic regression. Out of all four models, the proposed SCoVNet architecture reached the best performance with an accuracy of almost 99% and 98% on chest computed tomography scan images and chest X-ray images, respectively.

Suggested Citation

  • Ahatsham Hayat & Preety Baglat & Fábio Mendonça & Sheikh Shanawaz Mostafa & Fernando Morgado-Dias, 2023. "Novel Comparative Study for the Detection of COVID-19 Using CT Scan and Chest X-ray Images," IJERPH, MDPI, vol. 20(2), pages 1-14, January.
    • Handle: RePEc:gam:jijerp:v:20:y:2023:i:2:p:1268-:d:1031212
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    References listed on IDEAS

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    1. Panwar, Harsh & Gupta, P.K. & Siddiqui, Mohammad Khubeb & Morales-Menendez, Ruben & Bhardwaj, Prakhar & Singh, Vaishnavi, 2020. "A deep learning and grad-CAM based color visualization approach for fast detection of COVID-19 cases using chest X-ray and CT-Scan images," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
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