Research Output
Transfer learning-based method for detection of COVID-19 using X-Ray Images
  In this paper, we have performed transfer learning using different pre-trained convolutional neural networks for binary classification of X-ray images into COVID-19 disease and normal. The dataset is gathered from two open sources. Our dataset is consisting of 254 COVID-19 and 310 Normal X-ray images. The pandemic situation all around the world demands an efficient solution so that the disturbance of global health, daily life, and economy can be controlled. In this regard, we introduced the deep feature fusion-based technique which could help to design an embedded system. We fine-tuned and trained the thirteen independent pre-trained models and we found that the Resnet50V2 model performed efficiently for binary classification scenarios. Our proposed technique using transfer learning gives a detection rate of 99.5% for binary classification (Normal and COVID).

  • Date:

    28 December 2021

  • Publication Status:

    Published

  • Publisher

    IEEE

  • DOI:

    10.1109/icrai54018.2021.9651463

  • Cross Ref:

    10.1109/icrai54018.2021.9651463

  • Funders:

    Edinburgh Napier Funded

Citation

Rehman, A., Tariq, Z., Jan, S. U., Aziz, S., Khan, M. U., & Chaudry, H. N. (2021). Transfer learning-based method for detection of COVID-19 using X-Ray Images. In 2021 International Conference on Robotics and Automation in Industry (ICRAI). https://doi.org/10.1109/icrai54018.2021.9651463

Authors

Keywords

transfer-learning, COVID-19, deep feature extraction, feature selection, k-nearest neighbor

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