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

Robert Turnbull
3/16/2023
eess.IVcs.CVcs.LG

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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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Cite this paper

@article{turnbull2023enhanced,
  title  = {Enhanced detection of the presence and severity of COVID-19 from CT scans using lung segmentation},
  author = {Robert Turnbull},
  year   = {2023},
  eprint = {2303.09440},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2303.09440v2}
}

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