Your browser doesn't support javascript.
Fruit-CoV: An efficient vision-based framework for speedy detection and diagnosis of SARS-CoV-2 infections through recorded cough sounds.
Nguyen, Long H; 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.
  • Nguyen LH; Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Vietnam.
  • Pham NT; Division of Computational Mechatronics, Institute for Computational Science, Ton Duc Thang University, Ho Chi Minh City, Vietnam.
  • Do VH; Faculty of Electrical and Electronics Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam.
  • Nguyen LT; ASICLAND, Suwon, South Korea.
  • Nguyen TT; Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Vietnam.
  • Nguyen H; Human Computer Interaction Lab, Sejong University, Seoul, South Korea.
  • Nguyen ND; Khoury College of Computer Sciences, Northeastern University, Boston, USA.
  • Nguyen TT; Institute for Intelligent Systems Research and Innovation, Deakin University, Victoria, Australia.
  • Nguyen SD; School of Information Technology, Deakin University, Victoria, Australia.
  • Bhatti A; Laboratory for Computational Mechatronics, Institute for Computational Science and Artificial Intelligence, Van Lang University, Ho Chi Minh City, Vietnam.
  • Lim CP; Faculty of Mechanical-Electrical and Computer Engineering, Van Lang University, Ho Chi Minh City, Vietnam.
Expert Syst Appl ; 213: 119212, 2023 Mar 01.
Article in English | MEDLINE | ID: covidwho-2104913
ABSTRACT
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.
Keywords

Full text: Available Collection: International databases Database: MEDLINE Type of study: Experimental Studies / Prognostic study Topics: Variants Language: English Journal: Expert Syst Appl Year: 2023 Document Type: Article Affiliation country: J.eswa.2022.119212

Similar

MEDLINE

...
LILACS

LIS


Full text: Available Collection: International databases Database: MEDLINE Type of study: Experimental Studies / Prognostic study Topics: Variants Language: English Journal: Expert Syst Appl Year: 2023 Document Type: Article Affiliation country: J.eswa.2022.119212