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

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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.

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Data Availability

The datasets used during the current study are freely available in the UCI repository.

Code Availability

The code will be available upon request to reviewers.

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Author notes

  1. Yassine Akhiat, Kaouthar Touchanti, and Souad Amjad contributed equally to this work.

Authors and Affiliations

  1. TIMS, FS, Abdelmalek Essaadi University, Tetouan, Morocco

    Younes Bouchlaghem & Souad Amjad

  2. Departement of informatics, Faculty of Sciences, USMBA, Fez, Morocco

    Yassine Akhiat & Kaouthar Touchanti

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  1. Younes Bouchlaghem
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  2. Yassine Akhiat
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  3. Kaouthar Touchanti
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  4. Souad Amjad
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YB and YA wrote the main manuscript. KT proposed the methodology and prepared the figures. All authors reviewed the manuscript.

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Correspondence to Younes Bouchlaghem.

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Bouchlaghem, Y., Akhiat, Y., Touchanti, K. et al. LSAE: Autoencoder Latent Space for Dimensionality Reduction-Based Approach for COVID-19 Classification and Detection Task Using Chest X-ray. Oper. Res. Forum 4, 95 (2023). https://doi.org/10.1007/s43069-023-00278-5

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