Cov3d: Detection of the presence and severity of COVID-19 from CT scans using 3D ResNets
This work addresses the need for automated COVID-19 diagnosis and severity assessment from medical imaging, but it is incremental as it builds on existing 3D ResNet methods applied to a specific dataset.
The paper tackles the problem of detecting COVID-19 presence and severity from chest CT scans using a 3D convolutional neural network called Cov3d, achieving a macro F1 score of 0.9476 for presence detection and 0.7552 for severity classification, which improves on baseline competition results.
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.