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Electrical Engineering and Systems Science > Image and Video Processing

arXiv:2206.15073 (eess)
COVID-19 e-print

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[Submitted on 30 Jun 2022 (v1), last revised 17 Aug 2022 (this version, v2)]

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

Authors:Daniel Kienzle, Julian Lorenz, Robin Schön, Katja Ludwig, Rainer Lienhart
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Abstract: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.
Comments: 17 pages, 3 figures, informations about challenge submission
Subjects: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2206.15073 [eess.IV]
  (or arXiv:2206.15073v2 [eess.IV] for this version)
  https://doi.org/10.48550/arXiv.2206.15073

Submission history

From: Daniel Kienzle [view email]
[v1] Thu, 30 Jun 2022 07:09:28 UTC (96 KB)
[v2] Wed, 17 Aug 2022 14:38:43 UTC (2,125 KB)
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