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MSCCov19Net: multi-branch deep learning model for COVID-19 detection from cough sounds by Sezer Ulukaya; Ahmet Alp Sarıca; Oğuzhan Erdem; Ali Karaali is a Medicine article available to read on EtoBox.

What is MSCCov19Net: multi-branch deep learning model for COVID-19 detection from cough sounds about?

Coronavirus has an impact on millions of lives and has been added to the important pandemics that continue to affect with its variants. Since it is transmitted through the respiratory tract, it has had significant effects on public health and social relations. Isolating people who are COVID positive can minimize the transmission, therefore several exams are proposed to detect the virus such as reverse transcription-polymerase chain reaction (RT-PCR), chest X-Ray, and computed tomography (CT). However, these methods suffer from either a low detection rate or high radiation dosage, along with being expensive. In this study, deep neural network-based model capable of detecting coronavirus from only coughing sound, which is fast, remotely operable and has no harmful side effects, has been proposed. The proposed multi-branch model takes Mel Frequency Cepstral Coefficients (MFCC), Spectrogram, and Chromagram as inputs and is abbreviated as MSCCov19Net. The system is trained on publicly available crowdsourced datasets, and tested on two unseen (used only for testing) clinical and non-clinical datasets. Experimental outcomes represent that the proposed system outperforms the 6 popular deep

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Author

Sezer Ulukaya; Ahmet Alp Sarıca; Oğuzhan Erdem; Ali Karaali

Publisher

Springer Science and Business Media LLC

Published

2023

Language

EN

Field

Medicine (Health Sciences)