arXiv Open Access 2022

COVID Detection and Severity Prediction with 3D-ConvNeXt and Custom Pretrainings

Daniel Kienzle Julian Lorenz Robin Schön Katja Ludwig Rainer Lienhart

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Abstrak

Since COVID strongly affects the respiratory system, lung CT-scans can be used for the analysis of a patients health. We introduce a neural network for the prediction of the severity of lung damage and the detection of a COVID-infection using three-dimensional CT-data. Therefore, we adapt the recent ConvNeXt model to process three-dimensional data. Furthermore, we design and analyze different pretraining methods specifically designed to improve the models ability to handle three-dimensional CT-data. We rank 2nd in the 1st COVID19 Severity Detection Challenge and 3rd in the 2nd COVID19 Detection Challenge.

Topik & Kata Kunci

eess.IV cs.CV

Penulis (5)

Daniel Kienzle

Julian Lorenz

Robin Schön

Katja Ludwig

Rainer Lienhart

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APA MLA BibTeX

Kienzle, D., Lorenz, J., Schön, R., Ludwig, K., Lienhart, R. (2022). COVID Detection and Severity Prediction with 3D-ConvNeXt and Custom Pretrainings. https://arxiv.org/abs/2206.15073

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Informasi Jurnal

Tahun Terbit: 2022
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓