Cov3d: Detection of the presence and severity of COVID-19 from CT scans using 3D ResNets

• URL: http://arxiv.org/abs/2207.12218v1
• Date: Tue, 5 Jul 2022 05:22:38 GMT
• Title: Cov3d: Detection of the presence and severity of COVID-19 from CT scans using 3D ResNets
• Authors: Robert Turnbull
• Abstract summary: Cov3d is a three dimensional convolutional neural network for detecting the presence and severity of COVID19 from chest CT scans. For the task of classifying the severity of COVID19, it achieves a macro f1 score of 0.7552.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep learning has been used to assist in the analysis of medical imaging. One such use is the classification of Computed Tomography (CT) scans when detecting for COVID-19 in subjects. This paper presents Cov3d, a three dimensional convolutional neural network for detecting the presence and severity of COVID19 from chest CT scans. Trained on the COV19-CT-DB dataset with human expert annotations, it achieves a macro f1 score of 0.9476 on the validation set for the task of detecting the presence of COVID19. For the task of classifying the severity of COVID19, it achieves a macro f1 score of 0.7552. Both results improve on the baseline results of the `AI-enabled Medical Image Analysis Workshop and Covid-19 Diagnosis Competition' (MIA-COV19D) in 2022.

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