With COVID-19 cases rising rapidly, deep learning has emerged as a promising diagnosis technique. However, identifying the most accurate models to characterize COVID-19 patients is challenging because comparing results obtained with different types of data and acquisition processes is non-trivial. In this paper we designed, evaluated, and compared the performance of 20 convolutional neutral networks in classifying patients as COVID-19 positive, healthy, or suffering from other pulmonary lung infections based on Chest CT scans, serving as the first to consider the EfficientNet family for COVID-19 diagnosis and employ intermediate activation maps for visualizing model performance. All models are trained and evaluated in Python using 4173 Chest CT images from the dataset entitled "A COVID multiclass dataset of CT scans," with 2168, 758, and 1247 images of patients that are COVID-19 positive, healthy, or suffering from other pulmonary infections, respectively. EfficientNet-B5 was identified as the best model with an F1 score of 0.9769+/-0.0046, accuracy of 0.9759+/-0.0048, sensitivity of 0.9788+/-0.0055, specificity of 0.9730+/-0.0057, and precision of 0.9751 +/- 0.0051. On an alternate 2-class dataset, EfficientNetB5 obtained an accuracy of 0.9845+/-0.0109, F1 score of 0.9599+/-0.0251, sensitivity of 0.9682+/-0.0099, specificity of 0.9883+/-0.0150, and precision of 0.9526 +/- 0.0523. Intermediate activation maps and Gradient-weighted Class Activation Mappings offered human-interpretable evidence of the model's perception of ground-class opacities and consolidations, hinting towards a promising use-case of artificial intelligence-assisted radiology tools. With a prediction speed of under 0.1 seconds on GPUs and 0.5 seconds on CPUs, our proposed model offers a rapid, scalable, and accurate diagnostic for COVID-19.

翻译：随着COVID-19病例迅速上升,深层次的学习已成为一个很有希望的诊断技术,然而,确定用于COVID-19病人特征的最精确模型具有挑战性,因为将获得的结果与不同类型数据和购置过程进行比较是非三相的。在本文件中,我们设计、评价并比较了20个革命中立网络在根据切斯特CT扫描将病人归类为COVID-19呈阳性、健康或患有其他肺部感染的情况方面的表现。 作为第一个考虑COVID-19诊断有效网络大家庭的首选,并采用中间激活地图进行可视化模型表现。所有模型都利用了4173 Chest CT图像进行Python的培训和评价。我们设计、评价了208和1247个神经中将病人归类为COVID-19呈阳性、健康或患有其他肺部感染的模型。