Corpus ID: 218889750

Towards computer-aided severity assessment: training and validation of deep neural networks for geographic extent and opacity extent scoring of chest X-rays for SARS-CoV-2 lung disease severity

@article{Wong2020TowardsCS,
  title={Towards computer-aided severity assessment: training and validation of deep neural networks for geographic extent and opacity extent scoring of chest X-rays for SARS-CoV-2 lung disease severity},
  author={Alexander Wong and Zhong Qiu Lin and Lechen Wang and Audrey G. Chung and Beiyi Shen and Almas Abbasi and Mahsa Hoshmand-Kochi and Timothy Q. Duong},
  journal={ArXiv},
  year={2020},
  volume={abs/2005.12855}
}
  • Alexander Wong, Zhong Qiu Lin, +5 authors Timothy Q. Duong
  • Published 2020
  • Computer Science, Engineering, Medicine
  • ArXiv
  • Background: A critical step in effective care and treatment planning for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the assessment of the severity of disease progression. Chest x-rays (CXRs) are often used to assess SARS-CoV-2 severity, with two important assessment metrics being extent of lung involvement and degree of opacity. In this proof-of-concept study, we assess the feasibility of computer-aided scoring of CXRs of SARS-CoV-2 lung disease severity using a deep… CONTINUE READING

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