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

Younes Bouchlaghem (), Yassine Akhiat (), Kaouthar Touchanti () and Souad Amjad ()
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Younes Bouchlaghem: Abdelmalek Essaadi University
Yassine Akhiat: USMBA
Kaouthar Touchanti: USMBA
Souad Amjad: Abdelmalek Essaadi University

SN Operations Research Forum, 2023, vol. 4, issue 4, 1-23

Abstract: 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.

Keywords: COVID-19; Image classification; Chest X-ray; Autoencoder; Deep learning; Latent space; Dimensionality reduction; Feature selection (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s43069-023-00278-5

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