Abstract：Radiological diagnosis is an important part of the diagnosis and treatment of coronavirus disease 2019 (COVID-19).
However, the amount of CT image data is large and the time taken by a single patient is long, which brings great pressure to the
diagnosis of doctors. Based on the desensitized CT images of patients with COVID-19 in different hospitals, this study proposed
an image detection model based on convolution of time-space series by learning the texture of the sample lesions through depth
learning. This model could quickly locate the focus area in the CT image, and associate with the different stages of CT image of the
same patient, so as to get more accurate detection results. This study helpful to improve the efficiency of preliminary diagnosis and
differential diagnosis of COVID-19, which can be used to assist clinical diagnosis and contribute to disease control.
祖莅惠1
，胡博奇1
，王平1
，张忠2
，刘景鑫1. 基于深度学习的新型冠状病毒肺炎CT征象检测研究[J]. 中国医疗设备, 2020, 35(6): 89-92.
ZU Lihui1
, HU Boqi1
, WANG Ping1
, ZHANG Zhong2
, LIU Jingxin1. Study of COVID-19 CT Imaging Detection Based on Deep Learning. China Medical Devices, 2020, 35(6): 89-92.
