Zhou Tao, Lu Huiling, Yang Zaoli, Qiu Shi, Huo Bingqiang, Dong Yali
School of Computer Science and Engineering, North minzu University, Yinchuan 750021, China.
Ningxia Key Laboratory of Intelligent Information and Big Data Processing, Yinchuan 750021, China.
Appl Soft Comput. 2021 Jan;98:106885. doi: 10.1016/j.asoc.2020.106885. Epub 2020 Nov 6.
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.
新型冠状病毒病（COVID-19）的快速检测对于预防传播和提高治疗效果具有积极作用。本文聚焦于COVID-19的快速检测。我们提出了一种用于从CT图像中检测新型COVID-19的集成深度学习模型。从先前的出版物、权威媒体报道和公共数据库中获取了2933例COVID-19患者的肺部CT图像。对这些图像进行预处理以获得2500张高质量图像。从一家医院获取了2500张肺部肿瘤CT图像和2500张正常肺部CT图像。使用迁移学习来初始化模型参数并预训练三个深度卷积神经网络模型：AlexNet、GoogleNet和ResNet。这些模型用于对所有图像进行特征提取。将Softmax用作全连接层的分类算法。通过相对多数投票获得集成分类器EDL-COVID。最后，将该集成分类器与三个组件分类器进行比较，以评估准确率、灵敏度、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。各项评估指标也更高。该算法能够更好地满足新型冠状病毒病COVID-19的快速检测需求。
