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

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.