Enhanced detection of the presence and severity of COVID-19 from CT scans using lung segmentation

• URL: http://arxiv.org/abs/2303.09440v2
• Date: Sun, 19 Mar 2023 09:02:32 GMT
• Title: Enhanced detection of the presence and severity of COVID-19 from CT scans using lung segmentation
• Authors: Robert Turnbull
• Abstract summary: This paper presents version 2 of Cov3d, a deep learning model submitted in the 2022 competition. It results in a validation macro F1 score for predicting the presence of COVID-19 in the CT scans at 93.2%. It gives a macro F1 score for predicting the severity of COVID-19 on the validation set for task 2 as 72.8% which is above the baseline of 38%.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Improving automated analysis of medical imaging will provide clinicians more options in providing care for patients. The 2023 AI-enabled Medical Image Analysis Workshop and Covid-19 Diagnosis Competition (AI-MIA-COV19D) provides an opportunity to test and refine machine learning methods for detecting the presence and severity of COVID-19 in patients from CT scans. This paper presents version 2 of Cov3d, a deep learning model submitted in the 2022 competition. The model has been improved through a preprocessing step which segments the lungs in the CT scan and crops the input to this region. It results in a validation macro F1 score for predicting the presence of COVID-19 in the CT scans at 93.2% which is significantly above the baseline of 74\%. It gives a macro F1 score for predicting the severity of COVID-19 on the validation set for task 2 as 72.8% which is above the baseline of 38%.

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