ArticleScientific reports2023
Augmentation strategies for an imbalanced learning problem on a novel COVID-19 severity dataset.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Deep learning-based keratoconus detection from Scheimpflug images.Biomedical optics express · 2025Article
- E-GuARD: expert-guided augmentation for the robust detection of compounds interfering with biological assays.Journal of cheminformatics · 2025Article
- Leveraging Machine Learning to Predict and Assess Disparities in Severe Maternal Morbidity in Maryland.Healthcare (Basel, Switzerland) · 2025Article
- Majority clustering for imbalanced image classification.PeerJ. Computer science · 2025Article
- Article
- A Deep Convolutional Neural Network Model for Lung Disease Detection Using Chest X-Ray Imaging.Pulmonary medicine · 2025Article
- A Machine Learning Model for the Prediction of COVID-19 Severity Using RNA-Seq, Clinical, and Co-Morbidity Data.Diagnostics (Basel, Switzerland) · 2024Article
- A Critical Assessment of Generative Models for Synthetic Data Augmentation on Limited Pneumonia X-ray Data.Bioengineering (Basel, Switzerland) · 2023Article
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8 authors.
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Abstract
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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