EXPLAINABLE-BY-DESIGN APPROACH FOR COVID-19 CLASSIFICATION VIA CT-SCAN

  1. Plamen Angelov
  2. Eduardo Soares

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Abstract

The COVID-19 disease has widely spread all over the world since the beginning of 2020. On January 30, 2020 the World Health Organization (WHO) declared a global health emergency. At the time of writing this paper the number of infected about 2 million people worldwide and took over 125,000 lives, the advanced public health systems of European countries as well as of USA were overwhelmed. In this paper, we propose an eXplainable Deep Learning approach to detect COVID-19 from computer tomography (CT) - Scan images. The rapid detection of any COVID-19 case is of supreme importance to ensure timely treatment. From a public health perspective, rapid patient isolation is also extremely important to curtail the rapid spread of the disease. From this point of view the proposed method offers an easy to use and understand tool to the front-line medics. It is of huge importance not only the statistical accuracy and other measures, but also the ability to understand and interpret how the decision was made. The results demonstrate that the proposed approach is able to surpass the other published results which were using standard Deep Neural Network in terms of performance. Moreover, it produce highly interpretable results which may be helpful for the early detection of the disease by specialists.

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    SciScore for 10.1101/2020.04.24.20078584: (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: Thank you for sharing your code and data.

    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.

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  2. Version published to 10.1101/2020.04.24.20078584v3 on medRxiv
  3. Version published to 10.1101/2020.04.24.20078584v2 on medRxiv
  4. Version published to 10.1101/2020.04.24.20078584v1 on medRxiv