A Hybrid Pipeline for Covid-19 Screening Incorporating Lungs Segmentation and Wavelet Based Preprocessing of Chest X-Rays

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Abstract

We have developed a two-module pipeline for the detection of SARS-CoV-2 from chest X-rays (CXRs). Module 1 is a traditional convnet that generates masks of the lungs overlapping the heart and large vasa. Module 2 is a hybrid convnet that preprocesses CXRs and corresponding lung masks by means of the Wavelet Scattering Transform, and passes the resulting feature maps through an Attention block and a cascade of Separable Atrous Multiscale Convolutional Residual blocks to produce a class assignment as Covid or non-Covid. Module 1 was trained on a public dataset of 6395 CXRs with radiologist annotated lung contours. Module 2 was trained on a dataset of 2362 non-Covid and 1435 Covid CXRs acquired at the Henry Ford Health System Hospital in Detroit. Six distinct cross-validation models, were combined into an ensemble model that was used to classify the CXR images of the test set. An intuitive graphic interphase allows for rapid Covid vs . non-Covid classification of CXRs, and generates high resolution heat maps that identify the affected lung regions.

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  1. This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/10480343.

    This review is the result of a Live Review organized and hosted by PREreview and JMIR Publications on September 2, 2022. The call was joined by 15 people, including reviewers, preprint authors, and facilitators.

    Summary 

    The authors of this study present a novel strategy and methodology to classify patients' chest X-ray (CXR) images as SARS-CoV-2 positive (covid+) and SARS-CoV-2 negative (covid-). The proposed method is presented as an alternative to existing CXR evaluation methods which require trained expertise and sophisticated equipment to be implemented. The methodology is based on an hybrid artificial intelligence (AI) pipeline dominantly using wavelet scattering processing for …

  2. SciScore for 10.1101/2022.03.13.22272311: (What is this?)

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

    Table 1: Rigor

    Ethicsnot detected.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    No key resources detected.


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    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:
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