Fruit-CoV An efficient vision-based framework for speedy detection and diagnosis of SARS-CoV-2 infections through recorded cough sounds

Fruit-CoV : An efficient vision-based framework for speedy detection and diagnosis of SARS-CoV-2 infections through recorded cough sounds

© 2022 Elsevier Ltd. All rights reserved.

Bibliographische Detailangaben
Veröffentlicht in:Expert systems with applications. - 1999. - 213(2023) vom: 01. März, Seite 119212
1. Verfasser: Nguyen, Long H (VerfasserIn)
Weitere Verfasser: Pham, Nhat Truong, Do, Van Huong, Nguyen, Liu Tai, Nguyen, Thanh Tin, Nguyen, Hai, Nguyen, Ngoc Duy, Nguyen, Thanh Thi, Nguyen, Sy Dzung, Bhatti, Asim, Lim, Chee Peng
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2023
Zugriff auf das übergeordnete Werk:Expert systems with applications
Schlagworte:Journal Article COVID-19 Deep learning Delta variant EfficientNet Log-Mel spectrogram Machine vision Neural network PANNs Recorded cough sounds mehr... Remote detection SARS-CoV-2 infections Self-testing service Sound classification Speedy detection Wavegram
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520 |a COVID-19 is an infectious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This deadly virus has spread worldwide, leading to a global pandemic since March 2020. A recent variant of SARS-CoV-2 named Delta is intractably contagious and responsible for more than four million deaths globally. Therefore, developing an efficient self-testing service for SARS-CoV-2 at home is vital. In this study, a two-stage vision-based framework, namely Fruit-CoV, is introduced for detecting SARS-CoV-2 infections through recorded cough sounds. Specifically, audio signals are converted into Log-Mel spectrograms, and the EfficientNet-V2 network is used to extract their visual features in the first stage. In the second stage, 14 convolutional layers extracted from the large-scale Pretrained Audio Neural Networks for audio pattern recognition (PANNs) and the Wavegram-Log-Mel-CNN are employed to aggregate feature representations of the Log-Mel spectrograms and the waveform. Finally, the combined features are used to train a binary classifier. In this study, a dataset provided by the AICovidVN 115M Challenge is employed for evaluation. It includes 7,371 recorded cough sounds collected throughout Vietnam, India, and Switzerland. Experimental results indicate that the proposed model achieves an Area Under the Receiver Operating Characteristic Curve (AUC) score of 92.8% and ranks first on the final leaderboard of the AICovidVN 115M Challenge. Our code is publicly available 
650 4 |a Journal Article 
650 4 |a COVID-19 
650 4 |a Deep learning 
650 4 |a Delta variant 
650 4 |a EfficientNet 
650 4 |a Log-Mel spectrogram 
650 4 |a Machine vision 
650 4 |a Neural network 
650 4 |a PANNs 
650 4 |a Recorded cough sounds 
650 4 |a Remote detection 
650 4 |a SARS-CoV-2 infections 
650 4 |a Self-testing service 
650 4 |a Sound classification 
650 4 |a Speedy detection 
650 4 |a Wavegram 
700 1 |a Pham, Nhat Truong  |e verfasserin  |4 aut 
700 1 |a Do, Van Huong  |e verfasserin  |4 aut 
700 1 |a Nguyen, Liu Tai  |e verfasserin  |4 aut 
700 1 |a Nguyen, Thanh Tin  |e verfasserin  |4 aut 
700 1 |a Nguyen, Hai  |e verfasserin  |4 aut 
700 1 |a Nguyen, Ngoc Duy  |e verfasserin  |4 aut 
700 1 |a Nguyen, Thanh Thi  |e verfasserin  |4 aut 
700 1 |a Nguyen, Sy Dzung  |e verfasserin  |4 aut 
700 1 |a Bhatti, Asim  |e verfasserin  |4 aut 
700 1 |a Lim, Chee Peng  |e verfasserin  |4 aut 
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773 1 8 |g volume:213  |g year:2023  |g day:01  |g month:03  |g pages:119212 
856 4 0 |u http://dx.doi.org/10.1016/j.eswa.2022.119212  |3 Volltext 
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