• Corpus ID: 239050189

CXR-Net: An Encoder-Decoder-Encoder Multitask Deep Neural Network for Explainable and Accurate Diagnosis of COVID-19 pneumonia with Chest X-ray Images

@article{Zhang2021CXRNetAE,
  title={CXR-Net: An Encoder-Decoder-Encoder Multitask Deep Neural Network for Explainable and Accurate Diagnosis of COVID-19 pneumonia with Chest X-ray Images},
  author={Xin Zhang and Liangxiu Han and Tam Sobeih and Lianghao Han and Nina C. Dempsey-Hibbert and Symeon Lechareas and Ascanio Tridente and Haoming Chen and Stephen White},
  journal={ArXiv},
  year={2021},
  volume={abs/2110.10813}
}
Accurate and rapid detection of COVID-19 pneumonia is crucial for optimal patient treatment. Chest X-Ray (CXR) is the first line imaging test for COVID-19 pneumonia diagnosis as it is fast, cheap and easily accessible. Inspired by the success of deep learning (DL) in computer vision, many DL-models have been proposed to detect COVID-19 pneumonia using CXR images. Unfortunately, these deep classifiers lack the transparency in interpreting findings, which may limit their applications in clinical… 

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