Computer Tomography (CT) imaging of the chest is a valid diagnosis tool to detect COVID-19 promptly and to control the spread of the disease. In this work we propose a light Convolutional Neural Network (CNN) design, based on the model of the SqueezeNet, for the efficient discrimination of COVID-19 CT images with respect to other community-acquired pneumonia and/or healthy CT images. The architecture allows to an accuracy of 85.03% with an improvement of about 3.2% in the first dataset arrangement and of about 2.1% in the second dataset arrangement. The obtained gain, though of low entity, can be really important in medical diagnosis and, in particular, for Covid-19 scenario. Also the average classification time on a high-end workstation, 1.25 s, is very competitive with respect to that of more complex CNN designs, 13.41 s, witch require pre-processing. The proposed CNN can be executed on medium-end laptop without GPU acceleration in 7.81 s: this is impossible for methods requiring GPU acceleration. The performance of the method can be further improved with efficient pre-processing strategies for witch GPU acceleration is not necessary.

胸部计算机断层扫描 (CT) 成像是及时检测 COVID-19 并控制疾病传播的有效诊断工具。在这项工作中，我们提出了一种基于 SqueezeNet 模型的轻型卷积神经网络 (CNN) 设计，用于有效区分 COVID-19 CT 图像与其他社区获得性肺炎和/或健康 CT 图像。该架构的准确度为 85.03%，在第一数据集排列中提高了约 3.2%，在第二数据集排列中提高了约 2.1%。所获得的增益虽然实体性较低，但在医学诊断中非常重要，特别是对于 Covid-19 场景。此外，高端工作站上的平均分类时间为 1.25 秒，与需要预处理的更复杂的 CNN 设计（13.41 秒）相比非常有竞争力。所提出的 CNN 可以在没有 GPU 加速的中端笔记本电脑上在 7.81 秒内执行：这对于需要 GPU 加速的方法来说是不可能的。通过有效的预处理策略可以进一步提高该方法的性能，而无需 GPU 加速。