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Fruit-CoV: An efficient vision-based framework for speedy detection and diagnosis of SARS-CoV-2 infections through recorded cough sounds
journal contribution
posted on 2023-02-05, 22:55 authored by LH Nguyen, NT Pham, VH Do, LT Nguyen, TT Nguyen, H Nguyen, ND Nguyen, Thanh Thi NguyenThanh Thi Nguyen, SD Nguyen, Asim BhattiAsim Bhatti, Chee Peng LimChee Peng LimCOVID-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.
History
Journal
Expert Systems with ApplicationsVolume
213Article number
ARTN 119212Location
United StatesPublisher DOI
ISSN
0957-4174eISSN
1873-6793Language
EnglishPublication classification
C1 Refereed article in a scholarly journalPublisher
PERGAMON-ELSEVIER SCIENCE LTDUsage metrics
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No categories selectedKeywords
Science & TechnologyTechnologyComputer Science, Artificial IntelligenceEngineering, Electrical & ElectronicOperations Research & Management ScienceComputer ScienceEngineeringSound classificationCOVID-19Recorded cough soundsDelta variantEfficientNetSARS-CoV-2 infectionsDeep learningNeural networkMachine visionRemote detectionSpeedy detectionPANNsLog-Mel spectrogramSelf-testing serviceWavegramARTIFICIAL-INTELLIGENCE AICORONAVIRUS COVID-19PreventionLungInfectious DiseasesEmerging Infectious Diseases2.4 Surveillance and distributionInfectionMathematical SciencesInformation and Computing Sciences
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