UFRC: A Unified Framework for Reliable COVID-19 Detection on Crowdsourced Cough Audio.
Annu Int Conf IEEE Eng Med Biol Soc
; 2022: 3418-3421, 2022 07.
Article
in English
| MEDLINE | ID: covidwho-2018752
ABSTRACT
We suggested a unified system with core components of data augmentation, ImageNet-pretrained ResNet-50, cost-sensitive loss, deep ensemble learning, and uncertainty estimation to quickly and consistently detect COVID-19 using acoustic evidence. To increase the model's capacity to identify a minority class, data augmentation and cost-sensitive loss are incorporated (infected samples). In the COVID-19 detection challenge, ImageNet-pretrained ResNet-50 has been found to be effective. The unified framework also integrates deep ensemble learning and uncertainty estimation to integrate predictions from various base classifiers for generalisation and reliability. We ran a series of tests using the DiCOVA2021 challenge dataset to assess the efficacy of our proposed method, and the results show that our method has an AUC-ROC of 85.43 percent, making it a promising method for COVID-19 detection. The unified framework also demonstrates that audio may be used to quickly diagnose different respiratory disorders.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Crowdsourcing
/
COVID-19
Type of study:
Diagnostic study
/
Prognostic study
Limits:
Humans
Language:
English
Journal:
Annu Int Conf IEEE Eng Med Biol Soc
Year:
2022
Document Type:
Article
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