Detecting COVID-19 from respiratory sound recordings with transformers

Idil Aytekin, Onat Dalmaz, Haydar Ankishan, Emine U. Saritas, Ulas Bagci, Tolga Cukur*, Haydar Celik

*Corresponding author for this work

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

Abstract

Auscultation is an established technique in clinical assessment of symptoms for respiratory disorders. Auscultation is safe and inexpensive, but requires expertise to diagnose a disease using a stethoscope during hospital or office visits. However, some clinical scenarios require continuous monitoring and automated analysis of respiratory sounds to pre-screen and monitor diseases, such as the rapidly spreading COVID-19. Recent studies suggest that audio recordings of bodily sounds captured by mobile devices might carry features helpful to distinguish patients with COVID-19 from healthy controls. Here, we propose a novel deep learning technique to automatically detect COVID-19 patients based on brief audio recordings of their cough and breathing sounds. The proposed technique first extracts spectrogram features of respiratory recordings, and then classifies disease state via a hierarchical vision transformer architecture. Demonstrations are provided on a crowdsourced database of respiratory sounds from COVID-19 patients and healthy controls. The proposed transformer model is compared against alternative methods based on state-of-the-art convolutional and transformer architectures, as well as traditional machine-learning classifiers. Our results indicate that the proposed model achieves on par or superior performance to competing methods. In particular, the proposed technique can distinguish COVID-19 patients from healthy subjects with over 94% AUC.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2022
Subtitle of host publicationComputer-Aided Diagnosis
EditorsKaren Drukker, Khan M. Iftekharuddin
PublisherSPIE
ISBN (Electronic)9781510649415
DOIs
StatePublished - 2022
EventMedical Imaging 2022: Computer-Aided Diagnosis - Virtual, Online
Duration: Mar 21 2022Mar 27 2022

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume12033
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2022: Computer-Aided Diagnosis
CityVirtual, Online
Period3/21/223/27/22

Keywords

  • breathing
  • cough
  • COVID-19
  • respiratory
  • sound
  • transformer

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Biomaterials
  • Radiology Nuclear Medicine and imaging

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