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Regenerated Image Texture Features for COVID-19 Detection in Lung Images

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Image Analysis (SCIA 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13885))

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Abstract

Covid-19 pandemic led to a worldwide pandemic and brought tremendous strain on patient testing facilities. In this paper, we aim to provide an automated rapid detection of Covid-19 infection using lung images. This will reduce the manual efforts required for speedy diagnosis. To achieve this we extract texture features from CT-Scans or X-ray images of suspected patients. These features are regenerated using graph-based techniques and employed for ascertaining the infection. Results show that our proposed approach achieves high accuracy along with high sensitivity for both types of radiology images.

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Authors and Affiliations

  1. The LNM Institute of Information Technology, Jaipur, 302031, India

    Ankita Sharma & Preety Singh

Authors
  1. Ankita Sharma
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  2. Preety Singh
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Corresponding author

Correspondence to Ankita Sharma .

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Editors and Affiliations

  1. Aalborg University, Aalborg, Denmark

    Rikke Gade

  2. Linköping University, Linköping, Sweden

    Michael Felsberg

  3. Tampere University, Tampere, Finland

    Joni-Kristian Kämäräinen

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© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Sharma, A., Singh, P. (2023). Regenerated Image Texture Features for COVID-19 Detection in Lung Images. In: Gade, R., Felsberg, M., Kämäräinen, JK. (eds) Image Analysis. SCIA 2023. Lecture Notes in Computer Science, vol 13885. Springer, Cham. https://doi.org/10.1007/978-3-031-31435-3_18

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  • DOI: https://doi.org/10.1007/978-3-031-31435-3_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-31434-6

  • Online ISBN: 978-3-031-31435-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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