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Machine Learning and Deep Learning-Based Detection and Analysis of COVID-19 in Chest X-Ray Images

  • 2023
  • OriginalPaper
  • Chapter
Published in:
Authors
Kunal Kumar
Harsh Shokeen
Shalini Gambhir
Ashwani Kumar
Amar Saraswat
Published in
International Conference on Innovative Computing and Communications
Publisher
Springer Nature Singapore

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Abstract

The chapter delves into the use of machine learning and deep learning techniques to detect and analyze COVID-19 in chest X-ray images. It discusses the challenges posed by the pandemic and the limitations of existing detection methods such as CT scans and RT-PCR tests. The authors explore the application of various pre-trained models like DenseNet201, CoroNet, and VGG16, as well as traditional machine learning algorithms, to classify normal and pneumonia-infected lungs. The study involves data preprocessing, model training, and performance evaluation using metrics such as precision, recall, F1-score, and accuracy. The chapter highlights the comparative performance analysis of different models and identifies the best-performing approach. It concludes by suggesting future research directions, including the use of different endpoints for prediction from various machine learning models.
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previous chapter Investigating Part-of-Speech Tagging in Khasi Using Naïve Bayes and Support Vector Machine
next chapter A Comprehensive Study of Machine Learning Techniques for Diabetic Retinopathy Detection
Title
Machine Learning and Deep Learning-Based Detection and Analysis of COVID-19 in Chest X-Ray Images
Authors
Kunal Kumar
Harsh Shokeen
Shalini Gambhir
Ashwani Kumar
Amar Saraswat
Copyright Year
2023
Publisher
Springer Nature Singapore
Book
International Conference on Innovative Computing and Communications

Print ISBN: 978-981-19-3678-4

Electronic ISBN: 978-981-19-3679-1

Copyright Year: 2023

DOI
https://doi.org/10.1007/978-981-19-3679-1_12
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