Augmentation strategies for an imbalanced learning problem on a novel COVID-19 severity dataset

Since the beginning of the COVID-19 pandemic, many different machine learning models have been developed to detect and verify COVID-19 pneumonia based on chest X-ray images. Although promising, binary models have only limited implications for medical treatment, whereas the prediction of disease seve...

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Published inScientific reports Vol. 13; no. 1; p. 18299
Main Authors Schaudt, Daniel, von Schwerin, Reinhold, Hafner, Alexander, Riedel, Pascal, Reichert, Manfred, von Schwerin, Marianne, Beer, Meinrad, Kloth, Christopher
Format Journal Article
LanguageEnglish
Published London Nature Publishing Group UK 25.10.2023
Nature Publishing Group
Nature Portfolio
Subjects
631/114/1305
631/114/2413
692/700/1421
COVID-19
Datasets
Deep learning
Humanities and Social Sciences
Medical treatment
multidisciplinary
Pandemics
Resource allocation
Science
Science (multidisciplinary)
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Summary:Since the beginning of the COVID-19 pandemic, many different machine learning models have been developed to detect and verify COVID-19 pneumonia based on chest X-ray images. Although promising, binary models have only limited implications for medical treatment, whereas the prediction of disease severity suggests more suitable and specific treatment options. In this study, we publish severity scores for the 2358 COVID-19 positive images in the COVIDx8B dataset, creating one of the largest collections of publicly available COVID-19 severity data. Furthermore, we train and evaluate deep learning models on the newly created dataset to provide a first benchmark for the severity classification task. One of the main challenges of this dataset is the skewed class distribution, resulting in undesirable model performance for the most severe cases. We therefore propose and examine different augmentation strategies, specifically targeting majority and minority classes. Our augmentation strategies show significant improvements in precision and recall values for the rare and most severe cases. While the models might not yet fulfill medical requirements, they serve as an appropriate starting point for further research with the proposed dataset to optimize clinical resource allocation and treatment.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-023-45532-2