COVID-19 auxiliary diagnostic model based on migration learning and multi-parameter fusion optimization
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摘要:
目的 针对传统CT影像诊断准确性不高和效率低下问题，探讨深度学习技术在影像学中辅助诊断COVID-19的模型研究。 方法 首先构建早期、进展期和重症期三类别的COVID-19影像学数据集，然后构建一个基于VGG-16迁移学习的诊断COVID-19的初始模型，最后通过逐步对全连接层网络结构、激活函数、损失函数、优化算法、学习率和样本批次大小的多参数融合优化，设计出一个COVID-19辅助诊断模型。 结果 在COVID-19影像学测试集上COVID-19辅助诊断模型的准确率为98.10%，其中早期、进展期和重症期样本的敏感度分别为0.97、1.00、0.97，F1-score分别为0.98、0.97、0.99。 结论 通过迁移学习和多参数融合优化策略，设计的COVID-19辅助诊断模型在测试集上有较高的准确率。在防控疫情时，辅助诊断模型能帮助医务工作者提高工作效率。 Abstract:Objective To address the problem of low accuracy and efficiency of traditional CT image diagnosis, this paper discusses the model of deep learning technology in assisting the diagnosis of covid-19 in imaging. Methods First of all, a COVID-19 imaging dataset was constructed for three categories: early stage, progressive stage and critical stage. Then, an initial model for diagnosing COVID-19 based on VGG-16 transfer learning was constructed. Finally, a COVID-19 aided diagnosis model is designed by gradually optimizing the multi parameter fusion of the full connection layer network structure, activation function, loss function, optimization algorithm, learning rate and sample batch size. Results The accuracy of the COVID-19 auxiliary diagnosis model on the COVID-19 imaging test set was 98.10% with sensitivities of 0.97, 1.00 and 0.97 for the early, progressive and severe samples, and F1-scores of 0.98, 0.97 and 0.99, respectively. Conclusion Through migration learning and multi-parameter fusion optimization strategies, the designed COVID-19 aided diagnosis model had high accuracy on the test set. The assisted diagnosis model can help medical workers to improve their efficiency when preventing and controlling epidemics. -
Key words:
- transfer learning /
- deep learning /
- assisted diagnosis /
- COVID-19 /
- CT
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图 1 机器学习与迁移学习间不同的学习方式
A:机器学习；B：迁移学习
Figure 1. Differences between machine learning and transfer learning.
图 3 不同神经元个数的识别准确率与损失函数的变化曲线
A：准确率随迭代次数增加的变化曲线；B：损失函数随迭代次数的收敛曲线.
Figure 3. Variation curves of recognition accuracy and loss function for different numbers of neurons.
图 4 不同损失函数的识别准确率与损失函数的变化曲线
A:准确率随迭代次数增加的变化曲线; B:损失函数随迭代次数变化的收敛曲线.
Figure 4. Relationship between the recognition accuracy of different loss functions and the loss function.
图 5 不同学习率的识别准确率变化曲线
A:学习率为0.00001、0.00002、0.00003;B:学习率为0.00003、0.00004、0.00005; C:学习率为0.0001、0.0002、0.0003; D:学习率为0.00003、0.00005、0.0001.
Figure 5. Change curve of recognition accuracy rate with different learning rates.
图 6 不同样本批次大小的准确率与损失函数的变化曲线
A:不同样本批次大小准确率的变化曲线; B:不同样本批次大小损失函数的收敛曲线.
Figure 6. Variation curves of accuracy and loss function for different sample batch sizes.
图 7 不同优化器的准确率与损失函数变化曲线
A:不同优化器的准确率变化曲线; B:不同优化器的损失函数收敛曲线.
Figure 7. Curves of accuracy and loss function of different optimizers.
图 8 不同神经元个数识别准确率的变化曲线
Figure 8. Variation curve of the recognition accuracy of different neuron numbers.
图 9 不同神经元个数损失函数的收敛曲线
Figure 9. Convergence curves of loss functions for different neuron numbers.
图 10 COVID-19早期CT影像表现
COVID-19确诊患者，A:男性，42岁，咳嗽、咽痛和发热3 d，双肺多叶多灶分布（箭头所示）；B:女性，28岁，发热4 h，入院6 d右肺下叶后段胸膜下区扇形片状影（箭头所示）；C:男性，23岁，无临床症状，入院时左肺下叶孤立类圆形结节伴晕征（箭头所示）[30]
Figure 10. Early CT imaging performance of COVID-19.
图 11 COVID-19发展期CT影像表现
A~F：同一COVID-19患者处于发展期CT影像的表现，双肺GGO明显增大、增多，范围较大，可见“铺路石”征，部分实变，内可见空气支气管征；纵隔淋巴结未见肿大，未见胸腔积液征，未受累肺组织密度正常[31].
Figure 11. COVID-19 CT imaging performance during development.
图 12 不同病变分期患者胸部CT影像
A~B：女性，54岁，早期COVID-19患者（双下肺胸膜下散在斑片状GGO）；C~D，女性，54岁，进展期COVID-19患者（病灶累及多个肺叶，可见实变影及纤维条索影）；E~F，男性，78岁，重症期COVID-19患者（双肺弥漫性病变，实变影为主，可见“含气支气管征”）[32].
Figure 12. Chest CT images of patients with different pathological stages.
表 1 COVID-19影像学数据集的划分
Table 1. Division of COVID-19 imaging data set
数据集 样本个数 样本张量 训练集 412 412×224×224 测试集 105 105×224×224 
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表 2 COVID-19影像学数据集在CT表现的分布情况
Table 2. Distribution of the COVID-19 imaging data set on CT manifestations
CT表现 训练集 测试集 标注 早期 116 30 0 进展期 156 39 1 重症期 140 26 2
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表 3 评估模型常见的指标
Table 3. Common indicators of evaluation models
混淆矩阵 目标 指标 真实正样本 真实负样本 模型 预测正样本 TP FP 阳性预测值或精确率=TP/（TP+FP） 预测负样本 FN TN 阴性预测值=（TN）（/ TN + FN） 召回率=TP/（TP+FN） 特异性=TN/（TN+FP） 准确率= (TP+TN)/(TP+FP+FN+TN) 注：TP为正样本预测为正样本，FP为负样本预测为正样本，FN是预测为负的正样本，TN是预测为负的负样本，大多数情况下评估模型都要用到准确率，根据具体的应用侧重使用不同的评估指标.
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表 4 传统机器学习与各种不同迁移学习之间的关系
Table 4. Relationship between traditional machine learning and various transfer learning
学习框架 源域和目标域 源任务和目标任务 传统机器学习 相同 相同 迁移学习 归纳式迁移学习 相同 不同但相关 直推式迁移学习 不同但相关 不同但相关 无监督迁移学习 不同但相关 相同
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表 5 简单全连接层神经网络模型参数
Table 5. Model parameters of simple fully connected layer neural network
参数 批次大小 损失函数 学习率 优化器 神经元个数 激活函数 参数值 15 交叉熵 0.0001 AdagradOptimizer 2048 Relu
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表 6 全连接层神经网络模型参数
Table 6. Fully connected layer neural network model parameters
参数 批次大小 损失函数 学习率 优化器 两层神经元个数 激活函数 参数值 15 交叉熵 0.0001 AdagradOptimizer 2048、128 Relu
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表 7 三分类混淆矩阵
Table 7. Three-class confusion matrix (n)
混淆矩阵 预测类别 0 1 2 真实类别 0 29 1 0 1 0 39 0 2 0 1 35
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表 8 评估模型的指标
Table 8. Methods for evaluating models
评估方法 敏感度 精确率 F1-Score 类别 0 0.97 1.00 0.98 1 1 0.95 0.97 2 0.97 1.00 0.99
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