Abstract
During the Chinese Spring Festival travel rush in 2020, a new type of pneumonia disease, named COVID-19 subsequently broke out in Wuhan, Hubei province, China. The COVID-19 was quickly spreading in China and emerged nearly all over the world. In this chapter, our motivation is to adopt the deep learning techniques to help clinic doctors to diagnose the patients of COVID-19 and predict the trend of COVID-19. To realize our motivation, we on the one hand adopt deep learning techniques to analyse CT images of patients. The transfer learning and data augmentation techniques are adopted for the lacking of samples in our obtained CT image data set. We build a model by designing and training a new deep network to help clinic doctors to make an appropriate diagnose decision. On the other hand, according to the spreading characteristics of COVID-19 and the controlling measures adopted by Chinese government, we propose to modify the classic SEIR (susceptible-exposed-infectious-recovered) model and establish a new SEIR dynamics model with considering the infectiousness of the people in the latent period and the quarantine period. The appropriate parameters of our modified SEIR model are learned by using deep learning techniques. Our proposed deep learning-based diagnosis for COVID-19 can help medicine doctors to make an appropriate diagnostic decision. Our modified SEIR model can effectively predict the transmission trend of COVID-19 and can be used for short-term trend prediction of the epidemic.
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Acknowledgements
This work is supported by the NSFC under Grant No. 61673251, and by the NKRDPC under Grant No. 2016YFC0901900, and by the Fundamental Research Funds for the Central Universities under Grant No. GK201806013, and by the Innovation Funds for Graduate Programs in Shaanxi Normal University under Grant Nos. 2016CSY009 and 2018TS078.
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Xie, J., Wang, M., Liu, R. (2020). Deep Learning-Based COVID-19 Diagnosis and Trend Predictions. In: Joshi, A., Dey, N., Santosh, K. (eds) Intelligent Systems and Methods to Combat Covid-19. SpringerBriefs in Applied Sciences and Technology(). Springer, Singapore. https://doi.org/10.1007/978-981-15-6572-4_7
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DOI: https://doi.org/10.1007/978-981-15-6572-4_7
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