Custom Pretrainings and Adapted 3D-ConvNeXt Architecture for COVID Detection and Severity Prediction

• URL: http://arxiv.org/abs/2206.15073v1
• Date: Thu, 30 Jun 2022 07:09:28 GMT
• Title: Custom Pretrainings and Adapted 3D-ConvNeXt Architecture for COVID Detection and Severity Prediction
• Authors: Daniel Kienzle, Julian Lorenz, Robin Sch\"on, Katja Ludwig, Rainer Lienhart
• Abstract summary: We introduce an neural network for the prediction of the severity of lung damage and the detection of infection using three-dimensional CT-scans. In order to test the performance of our model, we participate in the 2nd COV19D Competition for severity prediction and infection detection.
• Score: 14.804451764265025
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Since COVID strongly affects the respiratory system, lung CT scans can be used for the analysis of a patients health. We introduce an neural network for the prediction of the severity of lung damage and the detection of infection using three-dimensional CT-scans. Therefore, we adapt the recent ConvNeXt model to process three-dimensional data. Furthermore, we introduce different pretraining methods specifically adjusted to improve the models ability to handle three-dimensional CT-data. In order to test the performance of our model, we participate in the 2nd COV19D Competition for severity prediction and infection detection.

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