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Analysis of COVID-19 Detection Algorithms Based on Convolutional Neural Network Models Using Chest X-ray Images

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Advances in Computing and Data Sciences (ICACDS 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1614))

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Abstract

COVID-19, a disease caused by corona virus is a worldwide pandemic which put millions into death. Not only on lives of people but also it has affected all countries in terms of development and wealth. The main challenge is to detect COVID-19 effectively with high accuracy. A fast classification algorithm can help the health professionals in many ways. This work focuses on the implementation analysis of various Convolutional Neural Network (CNN) which are pre-trained in detecting the disease from chest X-ray images with highest accuracy. Transfer learning is utilized, and fine tuning is performed to get a reliable classification of the image data. For the pre-trained CNN model Mobilenet-V2, highest accuracy of 94.298% and precision of 89.76% obtained.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Amrita Vishwa Vidyapeetham, Amritapuri, India

    Archana R. Nair & A. S. Remya Ajai

Authors
  1. Archana R. Nair
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  2. A. S. Remya Ajai
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Corresponding authors

Correspondence to Archana R. Nair or A. S. Remya Ajai .

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Editors and Affiliations

  1. University of KwaZulu-Natal, Durban, South Africa

    Mayank Singh

  2. Jaypee University of Engineering and Technology, Guna, India

    Vipin Tyagi

  3. Jaypee University of Information Technology, Waknaghat, India

    P. K. Gupta

  4. Institute of Information Theory and Automation, Prague, Czech Republic

    Jan Flusser

  5. University of Ottawa, Ottawa, ON, Canada

    Tuncer Ören

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Nair, A.R., Remya Ajai, A.S. (2022). Analysis of COVID-19 Detection Algorithms Based on Convolutional Neural Network Models Using Chest X-ray Images. In: Singh, M., Tyagi, V., Gupta, P.K., Flusser, J., Ören, T. (eds) Advances in Computing and Data Sciences. ICACDS 2022. Communications in Computer and Information Science, vol 1614. Springer, Cham. https://doi.org/10.1007/978-3-031-12641-3_5

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  • DOI: https://doi.org/10.1007/978-3-031-12641-3_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-12640-6

  • Online ISBN: 978-3-031-12641-3

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