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Empirical Evaluation of Deep Learning Models with Local Binary Pattern for COVID-19 Detection

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Advances in Data and Information Sciences

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 522))

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Abstract

COVID-19 (Coronavirus Disease 2019) has impacted many lives globally. Though vaccines have been found recently, many people lose their lives due to lack of early detection of the disease. Deep learning has gained significant interest in detecting COVID-19 through the use of image modalities. Conventional works have also attempted to attain better outcome in COVID-19 detection. However, they need further improvement with respect to accuracy. Applying image processing steps on data set before the application of deep learning (DL) model seems to improve the performance. The objective of proposed research work is to enhance the performance of deep learning models by using image denoising and feature extraction. Performance of proposed Local binary pattern (LBP)-DL is tested against conventional DL Models and found to be more efficient and accurate in COVID-19 detection.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, India

    P. Y. Sagar & Dhanya M. Dhanalakshmy

Authors
  1. P. Y. Sagar
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  2. Dhanya M. Dhanalakshmy
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Corresponding author

Correspondence to Dhanya M. Dhanalakshmy .

Editor information

Editors and Affiliations

  1. Krishna Engineering College, Ghaziabad, Uttar Pradesh, India

    Shailesh Tiwari

  2. Associate Professor, CSED, National Institute of Technology Agartala, Tripura, India

    Munesh C. Trivedi

  3. Faculty of Engineering and Science, University of Agder, Agder, Norway

    Mohan L. Kolhe

  4. Dept. of Computer Sci & Engineering, R. B. S. Engineering Technical Campus, Agra, Uttar Pradesh, India

    Brajesh Kumar Singh

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Sagar, P.Y., Dhanalakshmy, D.M. (2023). Empirical Evaluation of Deep Learning Models with Local Binary Pattern for COVID-19 Detection. In: Tiwari, S., Trivedi, M.C., Kolhe, M.L., Singh, B.K. (eds) Advances in Data and Information Sciences. Lecture Notes in Networks and Systems, vol 522. Springer, Singapore. https://doi.org/10.1007/978-981-19-5292-0_39

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