Detecting COVID-19 Related Pneumonia On CT Scans Using Hyperdimensional Computing.
Annu Int Conf IEEE Eng Med Biol Soc
; 2021: 3970-3973, 2021 11.
Article
in English
| MEDLINE | ID: covidwho-1566234
Preprint
This scientific journal article is probably based on a previously available preprint. It has been identified through a machine matching algorithm, human confirmation is still pending.
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This scientific journal article is probably based on a previously available preprint. It has been identified through a machine matching algorithm, human confirmation is still pending.
See preprint
ABSTRACT
Pneumonia is a common complication associated with COVID-19 infections. Unlike common versions of pneumonia that spread quickly through large lung regions, COVID-19 related pneumonia starts in small localized pockets before spreading over the course of several days. This makes the infection more resilient and with a high probability of developing acute respiratory distress syndrome. Because of the peculiar spread pattern, the use of pulmonary computerized tomography (CT) scans was key in identifying COVID-19 infections. Identifying uncommon pulmonary diseases could be a strong line of defense in early detection of new respiratory infection-causing viruses. In this paper we describe a classification algorithm based on hyperdimensional computing for the detection of COVID-19 pneumonia in CT scans. We test our algorithm using three different datasets. The highest reported accuracy is 95.2% with an F1 score of 0.90, and all three models had a precision of 1 (0 false positives).
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Pneumonia
/
COVID-19
Type of study:
Prognostic study
Limits:
Humans
Language:
English
Journal:
Annu Int Conf IEEE Eng Med Biol Soc
Year:
2021
Document Type:
Article
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