Deep Transfer Learning-Based COVID-19 Prediction Using Chest X-Rays

  1. Saurabh Kumar
  2. Shweta Mishra
  3. Sunil Kumar Singh

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Abstract

The novel coronavirus disease (COVID-19) is spreading very rapidly across the globe because of its highly contagious nature and is declared as a pandemic by the World Health Organization (WHO). Scientists are endeavouring to ascertain the drugs for its efficacious treatment. Because, until now, no full-proof drug is available to cure this deadly disease. Therefore, identifying COVID-19 positive people and quarantining them can be an effective solution to control its spread. Many machine learning and deep learning techniques are being used quite effectively to classify positive and negative cases. In this work, a deep transfer learning-based model is proposed to classify the COVID-19 cases using chest X-rays or CT scan images of infected persons. The proposed model is based on the ensembling of DenseNet121 and SqueezeNet1.0, which is named as DeQueezeNet. The model can extract the importance of various influential features from the X-ray images, which are effectively used to classify the COVID-19 cases. The performance study of the proposed model depicts its effectiveness in terms of accuracy and precision. A comparative study has also been done with the recently published works, and it is observed that the performance of the proposed model is significantly better.

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  1. Version published to 10.1177/09720634211050425
  2. ScreenIT

    SciScore for 10.1101/2020.05.12.20099937: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

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  3. Version published to 10.1101/2020.05.12.20099937 on medRxiv