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A COVID-19 Detection Algorithm Using Deep Features and Discrete Social Learning Particle Swarm Optimization for Edge Computing Devices

Published: 30 December 2021 Publication History

Abstract

COVID-19 has been spread around the world and has caused a huge number of deaths. Early detection of this disease is the most efficient way to prevent its rapid spread. Due to the development of internet technology and edge intelligence, developing an early detection system for COVID-19 in the medical environment of the Internet of Things (IoT) can effectively alleviate the spread of the disease. In this paper, a detection algorithm is developed, which can detect COVID-19 effectively by utilizing the features from Chest X-ray (CXR) images. First, a pre-trained model (ResNet18) is adopted for feature extraction. Then, a discrete social learning particle swarm optimization algorithm (DSLPSO) is proposed for feature selection. By filtering redundant and irrelevant features, the dimensionality of the feature vector is reduced. Finally, the images are classified by a Support Vector Machine (SVM) for COVID-19 detection. Experimental results show that the proposed algorithm can achieve competitive performance with fewer features, which is suitable for edge computing devices with lower computation power.

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  1. A COVID-19 Detection Algorithm Using Deep Features and Discrete Social Learning Particle Swarm Optimization for Edge Computing Devices

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    Published In

    cover image ACM Transactions on Internet Technology
    ACM Transactions on Internet Technology  Volume 22, Issue 3
    August 2022
    631 pages
    ISSN:1533-5399
    EISSN:1557-6051
    DOI:10.1145/3498359
    • Editor:
    • Ling Liu
    Issue’s Table of Contents

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 30 December 2021
    Accepted: 01 February 2021
    Revised: 01 December 2020
    Received: 01 September 2020
    Published in TOIT Volume 22, Issue 3

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    Author Tags

    1. COVID-19
    2. ResNet18
    3. DSLPSO
    4. SVM
    5. edge computing devices

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