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

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posted on 2022-07-05, 06:56 authored by Robert TurnbullRobert Turnbull


Deep learning has been used to assist in the analysis of medical imag- ing. 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 detect- ing 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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    University of Melbourne