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CheXNet for the Evidence of Covid-19 Using 2.3K Positive Chest X-rays

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Recent Trends in Image Processing and Pattern Recognition (RTIP2R 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1576))

Abstract

CheXNet is not a surprise for Deep Learning (DL) community as it was primarily designed for radiologist-level pneumonia detection in Chest X-rays (CXRs). In this paper, we study CheXNet to analyze CXRs to detect the evidence of Covid-19. On a dataset of size 4, 600 CXRs (2, 300 Covid-19 positive cases and 2, 300 non-Covid cases (Healthy and Pneumonia cases)) and with k(=5) fold cross-validation technique, we achieve the following performance scores: accuracy of 0.98, AUC of 0.99, specificity of 0.98 and sensitivity of 0.99. On such a large dataset, our results can be compared with state-of-the-art results.

Authors Credit Statement. Authors contributed equally to the paper.

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Notes

  1. 1.

    https://github.com/agchung/Figure1-COVID-chestxray-dataset.

  2. 2.

    https://github.com/agchung/Actualmed-COVID-chestxray-dataset.

  3. 3.

    https://www.kaggle.com/tawsifurrahman/covid19-radiography-database.

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Authors and Affiliations

  1. 2AI: Applied Artificial Intelligence Research Lab, Computer Science, University of South Dakota, Vermillion, SD, 57069, USA

    KC Santosh & Supriti Ghosh

Authors
  1. KC Santosh
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  2. Supriti Ghosh
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Corresponding authors

Correspondence to KC Santosh or Supriti Ghosh .

Editor information

Editors and Affiliations

  1. University of South Dakota, Vermillion, SD, USA

    KC Santosh

  2. Central University of Karnataka, Gulbarga, India

    Ravindra Hegadi

  3. Indian Statistical Institute, Kolkata, India

    Umapada Pal

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Santosh, K., Ghosh, S. (2022). CheXNet for the Evidence of Covid-19 Using 2.3K Positive Chest X-rays. In: Santosh, K., Hegadi, R., Pal, U. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2021. Communications in Computer and Information Science, vol 1576. Springer, Cham. https://doi.org/10.1007/978-3-031-07005-1_4

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  • DOI: https://doi.org/10.1007/978-3-031-07005-1_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-07004-4

  • Online ISBN: 978-3-031-07005-1

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