The rapid detection of the novel coronavirus disease, COVID-19, has a positive effect on preventing propagation and enhancing therapeutic outcomes. This article focuses on the rapid detection of COVID-19. We propose an ensemble deep learning model for novel COVID-19 detection from CT images. 2933 lung CT images from COVID-19 patients were obtained from previous publications, authoritative media reports, and public databases. The images were preprocessed to obtain 2500 high-quality images. 2500 CT images of lung tumor and 2500 from normal lung were obtained from a hospital. Transfer learning was used to initialize model parameters and pretrain three deep convolutional neural network models: AlexNet, GoogleNet, and ResNet. These models were used for feature extraction on all images. Softmax was used as the classification algorithm of the fully connected layer. The ensemble classifier EDL-COVID was obtained via relative majority voting. Finally, the ensemble classifier was compared with three component classifiers to evaluate accuracy, sensitivity, specificity, F value, and Matthews correlation coefficient. The results showed that the overall classification performance of the ensemble model was better than that of the component classifier. The evaluation indexes were also higher. This algorithm can better meet the rapid detection requirements of the novel coronavirus disease COVID-19.

快速检测新型冠状病毒疾病COVID-19，对预防疾病传播和提高治疗效果具有积极作用。本文重点介绍COVID-19的快速检测。我们提出了一种集成的深度学习模型，用于从CT图像中检测新型COVID-19。从以前的出版物，权威媒体报道和公共数据库中获得了来自COVID-19患者的2933幅肺部CT图像。对图像进行预处理以获得2500张高质量图像。从一家医院获得了2500例肺癌的CT图像和2500例正常肺的CT图像。转移学习用于初始化模型参数并预训练三个深层卷积神经网络模型：AlexNet，GoogleNet和ResNet。这些模型用于所有图像的特征提取。Softmax被用作全连接层的分类算法。整体分类器EDL-COVID是通过相对多数投票获得的。最后，将集成分类器与三个成分分类器进行比较，以评估准确性，敏感性，特异性，F值和马修斯相关系数。结果表明，集成模型的整体分类性能优于组件分类器。评价指标也较高。该算法可以更好地满足新型冠状病毒病COVID-19的快速检测要求。和Matthews相关系数。结果表明，集成模型的整体分类性能优于组件分类器。评价指标也较高。该算法可以更好地满足新型冠状病毒病COVID-19的快速检测要求。和Matthews相关系数。结果表明，集成模型的整体分类性能优于组件分类器。评价指标也较高。该算法可以更好地满足新型冠状病毒病COVID-19的快速检测要求。