Automated detection of COVID-19 using ensemble of transfer learning with deep convolutional neural network based on CT scans.
Automated detection of COVID-19 using ensemble of transfer learning with deep convolutional neural network based on CT scans.
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DOI：
10.1007/s11548-020-02286-w
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发表时间：
2021-01
影响因子：
3
通讯作者：
Vafaeezadeh M
中科院分区：
文献类型：
--
作者：
Gifani P;Shalbaf A;Vafaeezadeh M
COVID-19 已感染全球数百万人。控制这种疾病传播的最重要障碍之一是医疗检测效率低下和缺乏。计算机断层扫描 (CT) 扫描有望准确、快速地检测 COVID-19。然而，确定 COVID-19 需要训练有素的放射科医生，并且会受到观察者间差异的影响。为了弥补这些限制，本文引入了一种基于深度迁移学习集成的自动方法来检测 COVID-19。总共使用了 15 个预训练的卷积神经网络 (CNN) 架构：EfficientNets(B0-B5)、NasNetLarge、NasNetMobile、InceptionV3、ResNet-50、SeResnet 50、Xception、DenseNet121、ResNext50 和 Inception_resnet_v2，然后在目标任务上进行微调。之后，我们构建了一种基于深度迁移学习输出最佳组合的多数投票的集成方法，以进一步提高识别性能。我们使用了公开的 CT 扫描数据集，其中包含 349 个标记为 COVID-19 阳性的 CT 扫描和 397 个标记为正常或包含其他类型肺部疾病的 COVID-19 阴性 CT 扫描。实验结果表明，在通过 CT 扫描诊断 COVID-19 时，EfficientNetB0、EfficientNetB3、EfficientNetB5、Inception_resnet_v2 和 Xception 5 种深度迁移学习架构的多数投票结果比单个迁移学习结构和其他基于精度（0.857）、召回率（0.854）和准确性（0.85）指标的模型更高。我们的研究基于具有不同预训练 CNN 架构的集成深度迁移学习系统，可以在公开可用的 CT 图像数据集上很好地工作，用于基于 CT 扫描诊断 COVID-19。
COVID-19 has infected millions of people worldwide. One of the most important hurdles in controlling the spread of this disease is the inefficiency and lack of medical tests. Computed tomography (CT) scans are promising in providing accurate and fast detection of COVID-19. However, determining COVID-19 requires highly trained radiologists and suffers from inter-observer variability. To remedy these limitations, this paper introduces an automatic methodology based on an ensemble of deep transfer learning for the detection of COVID-19. A total of 15 pre-trained convolutional neural networks (CNNs) architectures: EfficientNets(B0-B5), NasNetLarge, NasNetMobile, InceptionV3, ResNet-50, SeResnet 50, Xception, DenseNet121, ResNext50 and Inception_resnet_v2 are used and then fine-tuned on the target task. After that, we built an ensemble method based on majority voting of the best combination of deep transfer learning outputs to further improve the recognition performance. We have used a publicly available dataset of CT scans, which consists of 349 CT scans labeled as being positive for COVID-19 and 397 negative COVID-19 CT scans that are normal or contain other types of lung diseases. The experimental results indicate that the majority voting of 5 deep transfer learning architecture with EfficientNetB0, EfficientNetB3, EfficientNetB5, Inception_resnet_v2, and Xception has the higher results than the individual transfer learning structure and among the other models based on precision (0.857), recall (0.854) and accuracy (0.85) metrics in diagnosing COVID-19 from CT scans. Our study based on an ensemble deep transfer learning system with different pre-trained CNNs architectures can work well on a publicly available dataset of CT images for the diagnosis of COVID-19 based on CT scans.
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