MSCCov19Net: multi-branch deep learning model for COVID-19 detection from cough sounds
Abstract 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 M el Frequency Cepstral Coefficients (MFCC), S pectrogram, and C hromagram 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 learning architectures on four datasets by representing a better generalization ability. The proposed system has reached an accuracy of 61.5 % in Virufy and 90.4 % in NoCoCoDa for unseen test datasets..
| Medienart: |
Artikel |
|---|
| Erscheinungsjahr: |
2023 |
|---|---|
| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:61 |
|---|---|
| Enthalten in: |
Medical & biological engineering & computing - 61(2023), 7 vom: 24. Feb., Seite 1619-1629 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Ulukaya, Sezer [VerfasserIn] |
|---|
| Links: |
Volltext [lizenzpflichtig] |
|---|
| Themen: |
|---|
| Anmerkungen: |
© International Federation for Medical and Biological Engineering 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
|---|
| doi: |
10.1007/s11517-023-02803-4 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
OLC2143952155 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | OLC2143952155 | ||
| 003 | DE-627 | ||
| 005 | 20240118092545.0 | ||
| 007 | tu | ||
| 008 | 240118s2023 xx ||||| 00| ||eng c | ||
| 024 | 7 | |a 10.1007/s11517-023-02803-4 |2 doi | |
| 035 | |a (DE-627)OLC2143952155 | ||
| 035 | |a (DE-He213)s11517-023-02803-4-p | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | 4 | |a 610 |a 660 |a 570 |q VZ |
| 084 | |a 12 |2 ssgn | ||
| 100 | 1 | |a Ulukaya, Sezer |e verfasserin |0 (orcid)0000-0003-0473-7547 |4 aut | |
| 245 | 1 | 0 | |a MSCCov19Net: multi-branch deep learning model for COVID-19 detection from cough sounds |
| 264 | 1 | |c 2023 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ohne Hilfsmittel zu benutzen |b n |2 rdamedia | ||
| 338 | |a Band |b nc |2 rdacarrier | ||
| 500 | |a © International Federation for Medical and Biological Engineering 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. | ||
| 520 | |a Abstract 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 M el Frequency Cepstral Coefficients (MFCC), S pectrogram, and C hromagram 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 learning architectures on four datasets by representing a better generalization ability. The proposed system has reached an accuracy of 61.5 % in Virufy and 90.4 % in NoCoCoDa for unseen test datasets. | ||
| 650 | 4 | |a Coronavirus | |
| 650 | 4 | |a Coughing | |
| 650 | 4 | |a Deep learning | |
| 650 | 4 | |a Ensemble learning | |
| 650 | 4 | |a Telehealth | |
| 700 | 1 | |a Sarıca, Ahmet Alp |4 aut | |
| 700 | 1 | |a Erdem, Oğuzhan |4 aut | |
| 700 | 1 | |a Karaali, Ali |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Medical & biological engineering & computing |d Springer Berlin Heidelberg, 1977 |g 61(2023), 7 vom: 24. Feb., Seite 1619-1629 |w (DE-627)129858552 |w (DE-600)282327-5 |w (DE-576)015165507 |x 0140-0118 |7 nnns |
| 773 | 1 | 8 | |g volume:61 |g year:2023 |g number:7 |g day:24 |g month:02 |g pages:1619-1629 |
| 856 | 4 | 1 | |u https://doi.org/10.1007/s11517-023-02803-4 |z lizenzpflichtig |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a SYSFLAG_A | ||
| 912 | |a GBV_OLC | ||
| 912 | |a SSG-OLC-TEC | ||
| 912 | |a SSG-OLC-CHE | ||
| 912 | |a SSG-OPC-MAT | ||
| 912 | |a GBV_ILN_2016 | ||
| 912 | |a GBV_ILN_2018 | ||
| 951 | |a AR | ||
| 952 | |d 61 |j 2023 |e 7 |b 24 |c 02 |h 1619-1629 | ||