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Local Binary Pattern and RVFL for Covid-19 Diagnosis

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Multimedia Technology and Enhanced Learning (ICMTEL 2023)

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Abstract

Recently, the use of artificial intelligence to improve the efficiency of Covid-19 diagnosis has become a trend due to the spread and proliferation of Covid-19 and the fact that healthcare professionals alone are no longer sufficient to cope with the rapid spread of Covid-19. Chest computed tomography (CT) is an effective method to diagnose Covid-19. Using image processing methods to help diagnose such images has become critical. In this trend, we propose a way to detect Covid-19 efficiently. The scheme employs a hybrid model. Local binary patterns (LBP) implement feature extraction in the preprocessing stage. Validation classification results are obtained using the random vector functional link (RVFL) network, which is finally validated by 10-fold cross-validation. It experimentally demonstrated the usefulness of our proposed model for diagnostic-level progress. It helps healthcare workers accurately identify Covid-19.

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Authors and Affiliations

  1. School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, 454000, Henan, People’s Republic of China

    Mengke Wang

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  1. Mengke Wang
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Correspondence to Mengke Wang .

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Editors and Affiliations

  1. Nanjing Normal University of Special Education, Nanjing, China

    Bing Wang

  2. Nanjing Normal University of Special Education, Nanjing, China

    Zuojin Hu

  3. Nanjing Normal University of Special Education, Nanjing, China

    Xianwei Jiang

  4. University of Leicester, Leicester, UK

    Yu-Dong Zhang

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© 2024 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Wang, M. (2024). Local Binary Pattern and RVFL for Covid-19 Diagnosis. In: Wang, B., Hu, Z., Jiang, X., Zhang, YD. (eds) Multimedia Technology and Enhanced Learning. ICMTEL 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 532. Springer, Cham. https://doi.org/10.1007/978-3-031-50571-3_23

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  • DOI: https://doi.org/10.1007/978-3-031-50571-3_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-50570-6

  • Online ISBN: 978-3-031-50571-3

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