Deep Learning for Automated Recognition of Covid-19 from Chest X-ray Images

  1. Linh T. Duong
  2. Phuong T. Nguyen
  3. Ludovico Iovino
  4. Michele Flammini

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Abstract

Background

The pandemic caused by coronavirus in recent months is having a devastating global effect, which puts the world under the most ever unprecedented emergency. Currently, since there are not effective antiviral treatments for Covid-19 yet, it is crucial to early detect and monitor the progression of the disease, thus helping to reduce mortality. While a corresponding vaccine is being developed, and different measures are being used to combat the virus, medical imaging techniques have also been investigated to assist doctors in diagnosing this disease.

Objective

This paper presents a practical solution for the detection of Covid-19 from chest X-ray (CXR) images, exploiting cutting-edge Machine Learning techniques.

Methods

We employ EfficientNet and MixNet, two recently developed families of deep neural networks, as the main classification engine. Furthermore, we also apply different transfer learning strategies, aiming at making the training process more accurate and efficient. The proposed approach has been validated by means of two real datasets, the former consists of 13,511 training images and 1,489 testing images, the latter has 14,324 and 3,581 images for training and testing, respectively.

Results

The results are promising: by all the experimental configurations considered in the evaluation, our approach always yields an accuracy larger than 95.0%, with the maximum accuracy obtained being 96.64%.

Conclusions

As a comparison with various existing studies, we can thus conclude that our performance improvement is significant.

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    SciScore for 10.1101/2020.08.13.20173997: (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.

    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 10.1101/2020.08.13.20173997v1 on medRxiv