mlsanityData to 7 Oct 2026

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

arXiv 2303.09440 ↗First posted 16 Mar 2023eess.IV cs.CV cs.LGcited by 1

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Abstract (arXiv)

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%.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 16 of its 30 references that are in the map. First posted on arXiv 16 Mar 2023.

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16 of 30 references.

References · 16

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4 more
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  1. 2024COVID-19 detection from pulmonary CT scans using a novel EfficientNet with attention mechanismcited by 0