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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
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    References listed on IDEAS

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    1. Panwar, Harsh & Gupta, P.K. & Siddiqui, Mohammad Khubeb & Morales-Menendez, Ruben & Singh, Vaishnavi, 2020. "Application of deep learning for fast detection of COVID-19 in X-Rays using nCOVnet," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    2. Georgia Dede & Evangelia Filiopoulou & Despo-Vaia Paroni & Christos Michalakelis & Thomas Kamalakis, 2023. "Analysis and Evaluation of Major COVID-19 Features: A Pairwise Comparison Approach," SN Operations Research Forum, Springer, vol. 4(1), pages 1-19, March.
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