  In this paper, a 3D-RegNet-based neural network is proposed for diagnosing
the physical condition of patients with coronavirus (Covid-19) infection. In
the application of clinical medicine, lung CT images are utilized by
practitioners to determine whether a patient is infected with coronavirus.
However, there are some laybacks can be considered regarding to this diagnostic
method, such as time consuming and low accuracy. As a relatively large organ of
human body, important spatial features would be lost if the lungs were
diagnosed utilizing two dimensional slice image. Therefore, in this paper, a
deep learning model with 3D image was designed. The 3D image as input data was
comprised of two-dimensional pulmonary image sequence and from which relevant
coronavirus infection 3D features were extracted and classified. The results
show that the test set of the 3D model, the result: f1 score of 0.8379 and AUC
value of 0.8807 have been achieved.
