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

2022-07-05 05:22:38
Robert Turnbull
arXiv_CV
             
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.

Abstract (translated)

URL

https://arxiv.org/abs/2207.12218

PDF

https://arxiv.org/pdf/2207.12218.pdf

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