Deep Learning with hyper-parameter tuning for COVID-19 Cough Detection

Sunil Rao, Vivek Narayanaswamy, Michael Esposito, Jayaraman Thiagarajan, Andreas Spanias

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

As the COVID-19 pandemic continues, rapid non-invasive testing has become essential. Recent studies and benchmarks motivates the use of modern artificial intelligence (AI) tools that utilize audio waveform spectral features of coughing for COVID-19 diagnosis. In this paper, we describe the system we developed for COVID-19 cough detection. We utilize features directly extracted from the coughing audio and use deep learning algorithms to develop automated diagnostic tools for COVID-19. In particular, we develop a unique modification of the VGG13 deep learning architecture for audio analysis that uses log-mel spectrograms and a combination of binary cross entropy and focal losses. This unique modification enabled the model to achieve highly robust classification of the DiCOVA 2021 COVID-19 data. We also explore the use of data augmentation and an ensembling strategy to further improve the performance on the validation and the blind test datasets. Our model achieved an average validation AUROC of 82.23% and a test AUROC of 78.3% at a sensitivity of 80.49%.

Original languageEnglish (US)
Title of host publicationIISA 2021 - 12th International Conference on Information, Intelligence, Systems and Applications
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665400329
DOIs
StatePublished - Jul 12 2021
Event12th International Conference on Information, Intelligence, Systems and Applications, IISA 2021 - Virtual, Chania Crete, Greece
Duration: Jul 12 2021Jul 14 2021

Publication series

NameIISA 2021 - 12th International Conference on Information, Intelligence, Systems and Applications

Conference

Conference12th International Conference on Information, Intelligence, Systems and Applications, IISA 2021
Country/TerritoryGreece
CityVirtual, Chania Crete
Period7/12/217/14/21

Keywords

  • COVID-19
  • acoustics
  • healthcare
  • machine learning
  • respiratory diagnosis

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Information Systems
  • Information Systems and Management

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