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Published in: New Generation Computing 1/2023

19-11-2022

Variational Autoencoder Based Imbalanced COVID-19 Detection Using Chest X-Ray Images

Authors: Sankhadeep Chatterjee, Soumyajit Maity, Mayukh Bhattacharjee, Soumen Banerjee, Asit Kumar Das, Weiping Ding

Published in: New Generation Computing | Issue 1/2023

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Abstract

Early and fast detection of disease is essential for the fight against COVID-19 pandemic. Researchers have focused on developing robust and cost-effective detection methods using Deep learning based chest X-Ray image processing. However, such prediction models are often not well suited to address the challenge of highly imabalanced datasets. The current work is an attempt to address the issue by utilizing unsupervised Variational Auto Encoders (VAEs). Firstly, chest X-Ray images are converted to a latent space by learning the most important features using VAEs. Secondly, a wide range of well established data resampling techniques are used to balance the preexisting imbalanced classes in the latent vector form of the dataset. Finally, the modified dataset in the new feature space is used to train well known classification models to classify chest X-Ray images into three different classes viz., ”COVID-19”, ”Pneumonia”, and ”Normal”. In order to capture the quality of resampling methods, 10-folds cross validation technique is applied on the dataset. Extensive experimental analysis have been carried out and results so obtained indicate significant improvement in COVID-19 detection using the proposed VAE based method. Furthermore, the ingenuity of the results have been established by performing Wilcoxon rank test with 95% level of significance.

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Metadata
Title
Variational Autoencoder Based Imbalanced COVID-19 Detection Using Chest X-Ray Images
Authors
Sankhadeep Chatterjee
Soumyajit Maity
Mayukh Bhattacharjee
Soumen Banerjee
Asit Kumar Das
Weiping Ding
Publication date
19-11-2022
Publisher
Springer Japan
Published in
New Generation Computing / Issue 1/2023
Print ISSN: 0288-3635
Electronic ISSN: 1882-7055
DOI
https://doi.org/10.1007/s00354-022-00194-y

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