IDEAS home Printed from https://ideas.repec.org/a/spr/snopef/v4y2023i4d10.1007_s43069-023-00278-5.html
   My bibliography  Save this article

LSAE: Autoencoder Latent Space for Dimensionality Reduction-Based Approach for COVID-19 Classification and Detection Task Using Chest X-ray

Author

Listed:
  • Younes Bouchlaghem

    (Abdelmalek Essaadi University)

  • Yassine Akhiat

    (USMBA)

  • Kaouthar Touchanti

    (USMBA)

  • Souad Amjad

    (Abdelmalek Essaadi University)

Abstract

The novel coronavirus 2019 (COVID-19) has rapidly spread, evolving into a global epidemic. Existing pharmaceutical techniques and diagnostic tests, such as reverse transcription–polymerase chain reaction (RT-PCR) and serology tests, are time-consuming, expensive, and require well-equipped laboratories for analysis. This restricts their accessibility to a broader population. The need for a simple and accurate screening method is imperative to identify infected individuals and curtail the virus’s propagation. In this paper, we introduce a novel COVID-19 classification and detection approach (LSAE, latent space autoencoder) based on chest X-ray image scans. Initially, the high dimensionality of input data is compressed into a reduced representation (latent space), preserving crucial features while discarding noise. This latent space subsequently serves as the input to build an efficient SVM classifier for COVID-19 detection. Experimental outcomes using the COVID-19 dataset are promising as they confirm the rapidity and detection capability of the proposed LSAE.

Suggested Citation

  • Younes Bouchlaghem & Yassine Akhiat & Kaouthar Touchanti & Souad Amjad, 2023. "LSAE: Autoencoder Latent Space for Dimensionality Reduction-Based Approach for COVID-19 Classification and Detection Task Using Chest X-ray," SN Operations Research Forum, Springer, vol. 4(4), pages 1-23, December.
    • Handle: RePEc:spr:snopef:v:4:y:2023:i:4:d:10.1007_s43069-023-00278-5
    DOI: 10.1007/s43069-023-00278-5
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s43069-023-00278-5
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s43069-023-00278-5?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:snopef:v:4:y:2023:i:4:d:10.1007_s43069-023-00278-5. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.