Detecting COVID-19 from respiratory sound recordings with transformers

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

doi:10.1117/12.2611490

4 April 2022 Detecting COVID-19 from respiratory sound recordings with transformers

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

Proceedings Volume 12033, Medical Imaging 2022: Computer-Aided Diagnosis; 1203306 (2022) https://doi.org/10.1117/12.2611490
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

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.

Conference Presentation

Citation Download Citation

Idil Aytekin, Onat Dalmaz, Haydar Ankishan, Emine U. Saritas, Ulas Bagci, Tolga Cukur, and Haydar Celik "Detecting COVID-19 from respiratory sound recordings with transformers", Proc. SPIE 12033, Medical Imaging 2022: Computer-Aided Diagnosis, 1203306 (4 April 2022); https://doi.org/10.1117/12.2611490

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