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More than 115 million individuals were affected by the coronavirus (COVID-19), which the World Health Organization classified as a pandemic. The virus fatally affected more than 2 million people. This has underscored the critical need for accurate and efficient diagnostic tools. In this paper, we provide a deep review of Convolutional Neural Network (CNN) based architectures and Ensembles for the detection of COVID-19 infection from medical imaging data. The study encompasses an in-depth analysis of the current state of the art, challenges, and advancements in the field, highlighting the potential of combining multiple models through ensemble methods for improved diagnostic accuracy.

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COVID-19 Detection Using Convolutional Neural Networks and Ensemble Approaches – A Review

  • 会议论文

  • 发表日期：

    2025年

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  • L. Agilandeeswari,
  • Abhimanyu Singh

摘要

More than 115 million individuals were affected by the coronavirus (COVID-19), which the World Health Organization classified as a pandemic. The virus fatally affected more than 2 million people. This has underscored the critical need for accurate and efficient diagnostic tools. In this paper, we provide a deep review of Convolutional Neural Network (CNN) based architectures and Ensembles for the detection of COVID-19 infection from medical imaging data. The study encompasses an in-depth analysis of the current state of the art, challenges, and advancements in the field, highlighting the potential of combining multiple models through ensemble methods for improved diagnostic accuracy.