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.

翻译：计算透视图像为放射学家诊断Covid-19提供了有用的信息。然而,对CT扫描的视觉分析很费时。因此,有必要从CT图像中开发自动检测Covid-19的算法。在本文中,我们建议建立一个基于信仰功能的共变神经网络,通过半监督培训来探测Covid-19案例。我们的方法首先提取深度特征,将其映射成信仰学位地图,并作出最后分类决定。我们的结果比传统的深层次学习分类模型的结果更可靠,更能解释。实验结果显示,我们的方法能够以0.81、0.812和0.875的精确度取得良好的性能。