Author:
Schaudt Daniel,von Schwerin Reinhold,Hafner Alexander,Riedel Pascal,Reichert Manfred,von Schwerin Marianne,Beer Meinrad,Kloth Christopher
Abstract
AbstractSince 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.
Funder
Technische Hochschule Ulm
Publisher
Springer Science and Business Media LLC
Reference84 articles.
1. Rubin, G. D. et al. The role of chest imaging in patient management during the COVID-19 pandemic: A multinational consensus statement from the Fleischner society. Radiology 296, 172–180. https://doi.org/10.1148/radiol.2020201365 (2020).
2. Hu, B., Guo, H., Zhou, P. & Shi, Z.-L. Characteristics of SARS-CoV-2 and COVID-19. Nat. Rev. Microbiol. 19, 141–154. https://doi.org/10.1038/s41579-020-00459-7 (2020).
3. Calvillo-Batllés, P. et al. Development of severity and mortality prediction models for COVID-19 patients at emergency department including the chest X-ray. Radiología (English Edition) 64, 214–227. https://doi.org/10.1016/j.rxeng.2021.09.004 (2022).
4. Cortes, C., Jackel, L. D., Solla, S. A., Vapnik, V. & Denker, J. S. Learning curves: Asymptotic values and rate of convergence, in Proceedings of the 6th international conference on neural information processing systems, NIPS’93, 327–334 (Morgan Kaufmann Publishers Inc., San Francisco, 1993).
5. Hestness, J. et al. Deep Learning Scaling is Predictable, Empirically. arXiv preprints: arXiv:1712.00409 (2017).
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