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The coronavirus pandemic is a serious health crisis caused by a virus called COVID-19. Rapidly spread over the world in 2019, scaring people everywhere. In addition to weariness, a dry cough, and fever, the virus targets the respiratory tract and causes pneumonia. As a result, pneumonia, lung cancer, or tuberculosis may all be mistakenly identified as these conditions. Timely detection is therefore very important, as COVID-19 can lead to patient impermanence. Chest X-ray (CXR) is a widely used medical practice for rapid and accurate diagnosis. In this proposed work, Mobilenetv2, Resnet50 and InceptionResnetv2 are used for the classification of different freezing of layers. This dataset is collected from Kaggle and the experiments are carried out by freezing distinct layers and employing transfer learning, which then classified into normal, pneumonia and COVID-19. The various results are obtained from Mobilenetv2, Resnet50 and InceptionResnetv2 and when comparing InceptionResnetv2 gives the highest accuracy of 83.44%.

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Detection and Classification of COVID-19 and Pneumonia Using Chest X-Ray Images

  • 会议论文

  • 发表日期：

    2025年

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  • R. Indhumathi,
  • P. Aurchana,
  • K. Nirmaladevi,
  • S. Sowmya,
  • A. Ramalingam,
  • G. Revathy

摘要

The coronavirus pandemic is a serious health crisis caused by a virus called COVID-19. Rapidly spread over the world in 2019, scaring people everywhere. In addition to weariness, a dry cough, and fever, the virus targets the respiratory tract and causes pneumonia. As a result, pneumonia, lung cancer, or tuberculosis may all be mistakenly identified as these conditions. Timely detection is therefore very important, as COVID-19 can lead to patient impermanence. Chest X-ray (CXR) is a widely used medical practice for rapid and accurate diagnosis. In this proposed work, Mobilenetv2, Resnet50 and InceptionResnetv2 are used for the classification of different freezing of layers. This dataset is collected from Kaggle and the experiments are carried out by freezing distinct layers and employing transfer learning, which then classified into normal, pneumonia and COVID-19. The various results are obtained from Mobilenetv2, Resnet50 and InceptionResnetv2 and when comparing InceptionResnetv2 gives the highest accuracy of 83.44%.