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    CoughNet-V2: A Scalable Multimodal DNN Framework for Point-of-Care Edge Devices to Detect Symptomatic COVID-19 Cough

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    CoughNet_V2 (1).pdf (709.2Kb)
    Links to Files
    https://ieeexplore.ieee.org/document/9744064
    Permanent Link
    https://doi.org/10.1109/HI-POCT54491.2022.9744064
    http://hdl.handle.net/11603/24683
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    • UMBC Computer Science and Electrical Engineering Department
    • UMBC Faculty Collection
    • UMBC Student Collection
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    Author/Creator
    Rashid, Hasib-Al
    Sajadi, Mohammad M.
    Mohsenin, Tinoosh
    Author/Creator ORCID
    https://orcid.org/0000-0002-9983-6929
    https://orcid.org/0000-0001-5551-2124
    Date
    2022-04-01
    Type of Work
    4 pages
    Text
    conference papers and proceedings
    postprints
    Citation of Original Publication
    H. -A. Rashid, M. M. Sajadi and T. Mohsenin, "CoughNet-V2: A Scalable Multimodal DNN Framework for Point-of-Care Edge Devices to Detect Symptomatic COVID-19 Cough," 2022 IEEE Healthcare Innovations and Point of Care Technologies (HI-POCT), 2022, pp. 37-40, doi: 10.1109/HI-POCT54491.2022.9744064.
    Rights
    © 2022 IEEE.  Personal use of this material is permitted.  Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Abstract
    With the emergence of COVID-19 pandemic, new attention has been given to different acoustic bio-markers of the respiratory disorders. Deep Neural Network (DNN) has become very popular with the audio classification task due to its impressive performance for speech detection, audio event classification etc. This paper presents CoughNet-V2 - a scalable multimodal DNN framework to detect symptomatic COVID-19 cough. The framework was designed to be implemented on point-of-care edge devices to help the doctors at pre-screening stage for COVID-19 detection. A crowd-sourced multimodal data resource which contains subjects’ cough audio along with other relevant medical information was used to design the CoughNet-V2 framework. CoughNet-V2 shows multimodal integration of cough audio along with medical records improves the classification performance than that of any unimodal frameworks. Proposed CoughNet-V2 achieved an area-under-curve (AUC) of 88.9% for the binary classification task of symptomatic COVID-19 cough detection. Finally, measurement of the deployment attributes of the CoughNet-V2 model onto processing components of an NVIDIA TX2 development board is presented as a proposition to bring the healthcare system to consumers’ fingertips.Clinical relevance—CoughNet-V2 will help medical practitioners to asses whether the patients need intensive medical help without physically interacting with them.


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    Albin O. Kuhn Library & Gallery
    University of Maryland, Baltimore County
    1000 Hilltop Circle
    Baltimore, MD 21250
    www.umbc.edu/scholarworks

    Contact information:
    Email: scholarworks-group@umbc.edu
    Phone: 410-455-3021


    If you wish to submit a copyright complaint or withdrawal request, please email mdsoar-help@umd.edu.