  Computed tomography (CT) image provides useful information for radiologists
to diagnose Covid-19. However, visual analysis of CT scans is time-consuming.
Thus, it is necessary to develop algorithms for automatic Covid-19 detection
from CT images. In this paper, we propose a belief function-based convolutional
neural network with semi-supervised training to detect Covid-19 cases. Our
method first extracts deep features, maps them into belief degree maps and
makes the final classification decision. Our results are more reliable and
explainable than those of traditional deep learning-based classification
models. Experimental results show that our approach is able to achieve a good
performance with an accuracy of 0.81, an F1 of 0.812 and an AUC of 0.875.
