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Analysis and Detection of COVID-19 Using Various CNN Models

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Part of the Algorithms for Intelligent Systems book series (AIS)


The novel Coronavirus pandemic hit the globe in December 2019. The virus has caused millions of deaths the world over. This has caused tremendous pressure on the healthcare system and social lives. Thus, the need to detect COVID at an early stage and also at a faster rate will help us fight against the virus. One of the primary reasons for the rapid spreading of the disease is not having the testing kits and also the time it takes to provide the result. Hence, using imaging techniques like chest X-rays will detect the virus quickly and effectively as lungs are affected when a person comes in contact with the virus. In our work, we used chest X-rays to detect COVID-19 as they are more affordable and available in every clinic. The Conventional Neural Network (CNN) algorithm was used to detect the virus. We analyzed four different models, and determined the results, confusion matrices, and accuracy of all models. The Xception model gave the best accuracy with 93%.


  • Machine learning
  • Deep learning
  • Artificial intelligence
  • CNN
  • COVID-19
  • Xception
  • VGG19
  • InceptionV3
  • ResNet50

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  • DOI: 10.1007/978-981-16-6460-1_12
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Correspondence to Madhuri Kommineni .

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Amrutha Tejaswini, M., Kommineni, M. (2022). Analysis and Detection of COVID-19 Using Various CNN Models. In: Jacob, I.J., Kolandapalayam Shanmugam, S., Bestak, R. (eds) Data Intelligence and Cognitive Informatics. Algorithms for Intelligent Systems. Springer, Singapore.

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