Towards Automated COVID-19 Presence and Severity Classification.
Stud Health Technol Inform
; 302: 917-921, 2023 May 18.
Article
in English
| MEDLINE | ID: covidwho-2325087
ABSTRACT
COVID-19 presence classification and severity prediction via (3D) thorax computed tomography scans have become important tasks in recent times. Especially for capacity planning of intensive care units, predicting the future severity of a COVID-19 patient is crucial. The presented approach follows state-of-theart techniques to aid medical professionals in these situations. It comprises an ensemble learning strategy via 5-fold cross-validation that includes transfer learning and combines pre-trained 3D-versions of ResNet34 and DenseNet121 for COVID19 classification and severity prediction respectively. Further, domain-specific preprocessing was applied to optimize model performance. In addition, medical information like the infection-lung-ratio, patient age, and sex were included. The presented model achieves an AUC of 79.0% to predict COVID-19 severity, and 83.7% AUC to classify the presence of an infection, which is comparable with other currently popular methods. This approach is implemented using the AUCMEDI framework and relies on well-known network architectures to ensure robustness and reproducibility.
Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
COVID-19
Type of study:
Experimental Studies
/
Prognostic study
/
Randomized controlled trials
Limits:
Humans
Language:
English
Journal:
Stud Health Technol Inform
Journal subject:
Medical Informatics
/
Health Services Research
Year:
2023
Document Type:
Article
Affiliation country:
Shti230309
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