ISSN 1004-4140
CN 11-3017/P
Volume 30 Issue 4
Sep.  2021
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WEI Dongxu, YAN Lihua, SHI Junqiang. COVID-19 Deep Learning Diagnosis Method Based on Attention Mechanism and Transfer Learning[J]. CT Theory and Applications, 2021, 30(4): 477-486. DOI: 10.15953/j.1004-4140.2021.30.04.08
Citation: WEI Dongxu, YAN Lihua, SHI Junqiang. COVID-19 Deep Learning Diagnosis Method Based on Attention Mechanism and Transfer Learning[J]. CT Theory and Applications, 2021, 30(4): 477-486. DOI: 10.15953/j.1004-4140.2021.30.04.08
shu

COVID-19 Deep Learning Diagnosis Method Based on Attention Mechanism and Transfer Learning

  • 1. Neonatal Intensive Care Unit, Qingdao Women and Children's Hospital, Qingdao, 266000, China;

  • 2. Institute of Oceanographic Instrumentation, Qilu University of Technology (Shandong Academy of Sciences), Qingdao, 266100, China

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  • Received Date: April 21, 2021
  • Available Online: September 23, 2021
    Graphical Abstract
    • Abstract
  • Objective: This paper proposed a lung CT image automatic diagnosis model under multi level spatial attention mechanism (ML-SAM) associated with new coronavirus (COVID-19) infection in combination with the correcting CT imaging features. Methods: The published lung CT dataset samples of COVID-19 patients were collected and utilized to construct a fusion model by incorporating the attention mechanism and transfer learning strategy into the deep network. Results: The fusion model established in this paper realizes the rapid and effective auxiliary diagnosis of COVID-19. In the test dataset, the correct recognition rate of the model for COVID-19 can reach 95%. Conclusion: The deep transfer learning model established in this paper can be used by radiologists or health care professionals as an artificial intelligence tool to quickly and accurately screen COVID-19 cases during the outbreak of COVID-19.
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