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基于数据融合的智能医疗辅助诊断方法

张桃红 范素丽 郭徐徐 李倩倩

张桃红, 范素丽, 郭徐徐, 李倩倩. 基于数据融合的智能医疗辅助诊断方法[J]. 工程科学学报. doi: 10.13374/j.issn2095-9389.2021.01.12.003
引用本文: 张桃红, 范素丽, 郭徐徐, 李倩倩. 基于数据融合的智能医疗辅助诊断方法[J]. 工程科学学报. doi: 10.13374/j.issn2095-9389.2021.01.12.003
ZHANG Tao-hong, FAN Su-li, GUO Xu-xu, LI Qian-qian. Intelligent medical assistant diagnosis method based on data fusion[J]. Chinese Journal of Engineering. doi: 10.13374/j.issn2095-9389.2021.01.12.003
Citation: ZHANG Tao-hong, FAN Su-li, GUO Xu-xu, LI Qian-qian. Intelligent medical assistant diagnosis method based on data fusion[J]. Chinese Journal of Engineering. doi: 10.13374/j.issn2095-9389.2021.01.12.003

基于数据融合的智能医疗辅助诊断方法

doi: 10.13374/j.issn2095-9389.2021.01.12.003
基金项目: 中央高校基本科研业务费专项资金资助项目(FRF-GF-20-16B)
详细信息
    通讯作者:

    E-mail:zth_ustb@163.com

  • 中图分类号: TG142.71

Intelligent medical assistant diagnosis method based on data fusion

More Information
  • 摘要: 医生诊断需要结合临床症状、影像检查等各种数据,基于此,提出了一种可以进行数据融合的医疗辅助诊断方法。将患者的影像信息(如CT图像)和数值数据(如临床诊断信息)相结合,利用结合的信息自动预测患者的病情,进而提出了基于深度学习的医疗辅助诊断模型。模型以卷积神经网络为基础进行搭建,图像和数值数据作为输入,输出病人的患病情况。该医疗辅助诊断方法能够利用更加全面的信息,有助于提高自动诊断准确率、降低诊断误差;另外,仅使用提出的医疗辅助诊断模型就可以一次性处理多种类型的数据,能够在一定程度上节省诊断时间。在两个数据集上验证了所提出方法的有效性,实验结果表明,该方法是有效的,它可以提高辅助诊断的准确性。

     

  • 图  1  基于提出的方法构建的模型结构

    Figure  1.  Diagram of the model structure based on the proposed method

    图  2  基本单元和下采样单元的结构。(a)基本单元的结构;(b)下采样单元的结构

    Figure  2.  Structure of the basic unit and down sampling unit:(a) structure of the basic unit; (b) structure of the down sampling unit

    图  3  训练过程中的预测准确率和损失的变动。(a)准确率的变动;(b)损失的变动

    Figure  3.  Changes in predictive accuracy and loss during training: (a) changes in accuracy; (b) changes in the loss

    图  4  COVID数据集中两种类型的样本。(a)未患COVID−19的样本;(b)患有COVID−19的样本

    Figure  4.  Two types of samples in COVID: (a) samples without COVID−19; (b) samples with COVID−19

    图  5  训练过程中的预测准确率和损失的变动。(a)准确率的变动;(b)损失的变动

    Figure  5.  Changes in predictive accuracy and loss during training: (a) changes in accuracy; (b) changes in the loss

    表  1  PHD中四种类型的样本

    Table  1.   Four types of samples in PHD

    ClassImageAgeSexCPTRBP/kPaSC/(mg·dL−1FBS/(mg·dL−1RERMHR/(times·min−1EIAST/mVSPNVThal
    PH6603202260111402.6002
    PNH541014.72390112612.8113
    NPH650220.72690114800.8202
    NPNH701017.33220010902.4132
    下载: 导出CSV

    表  2  在PHD数据集上仅通过图像数据学习的预测结果

    Table  2.   Prediction results learned only from image data in PHD dataset

    PredictionLabel
    No pneumoniaPneumoniaAll
    No pneumonia791190
    Pneumonia128092
    All9191182
    下载: 导出CSV

    表  3  在PHD数据集上仅通过结构化的数值数据学习的预测结果

    Table  3.   Prediction results learned only through structured numerical data

    PredictionLabel
    No pneumoniaPneumoniaAll
    No pneumonia721688
    Pneumonia118394
    All8399182
    下载: 导出CSV

    表  4  本文方法在PHD数据集上的预测结果

    Table  4.   Predictive results of proposed method in PHD dataset

    PredictionLabel
    NPNHNPHPNHPHAll
    NPNH33125252
    NPH8290039
    PNH22241038
    PH2393953
    All45463853182
    下载: 导出CSV

    表  5  在PHD数据集上三组实验的准确率和其他评价指标

    Table  5.   Accuracy and other evaluation indicators of three groups of experiments in PHD dataset

    ModelClassTPFPFNPrecisionRecallF1-scoreAccuracy
    Fusion methodNHPH3319120.6350.7330.6800.687
    NPH2910170.7440.6300.682
    PNH2414140.6320.6320.632
    PH3914140.7360.7360.736
    ShuffleNetv2(Only image data)No pneumonia7911120.8780.8680.8730.874
    Pneumonia8012110.8700.8790.874
    DNN(Only structured data)No heart disease7216110.8180.8670.8420.852
    Heart disease8311160.8830.8380.860
    下载: 导出CSV

    表  6  在COVID数据集上仅通过图像数据学习的预测结果

    Table  6.   Prediction results learned only from image data in COVID dataset

    PredictionLabel
    NonCOVIDCOVIDAll
    NonCOVID552075
    COVID144963
    All6969138
    下载: 导出CSV

    表  7  在COVID数据集上仅通过结构化的数值数据学习的预测结果

    Table  7.   Prediction results learned only by structured numerical data in COVID dataset

    PredictionLabel
    NonCOVIDCOVIDAll
    NonCOVID53053
    COVID166985
    All6969138
    下载: 导出CSV

    表  8  本文方法在COVID数据集上的预测结果

    Table  8.   Predictive results of proposed method in COVID dataset

    PredictionLabel
    NonCOVIDCOVIDAll
    NonCOVID65469
    COVID46569
    All6969138
    下载: 导出CSV

    表  9  在COVID数据集上三组实验的准确度和其他评价指标

    Table  9.   Accuracy and other evaluation indicators of three groups of experiments in COVID dataset

    ModelClassTPFPFNPrecisionRecallF1-scoreAccuracy
    Fusion methodNonCOVID65440.9420.9420.9420.942
    COVID65440.9420.9420.942
    ShuffleNetv2(Only image data)NonCOVID5520140.7330.7970.7640.754
    COVID4914200.7780.7100.742
    DNN(Only structured data)NonCOVID530161.000.7680.8690.884
    COVID691600.8121.000.896
    下载: 导出CSV

    表  10  本文方法和仅通过图像学习对138个样本进行分类的时间

    Table  10.   Time required to classify 138 samples using proposed method and using only image data

    ModelProposed methodImage only
    Time3.583.56
    下载: 导出CSV

    表  11  Fusion method、ResNet50、VGG16、ShuffleNetv2和AlexNet的准确度和其他评价指标

    Table  11.   Accuracy and other evaluation indicators of Fusion method, ResNet50, VGG16, ShuffleNetv2 and AlexNet

    ModelClassTPFPFNPrecisionRecallF1-scoreAccuracy
    Fusion methodNonCOVID65440.9420.9420.9420.942
    COVID65440.9420.9420.942
    ResNet50NonCOVID5615130.7890.8120.8000.797
    COVID5413150.8060.7830.794
    VGG16NonCOVID5416150.7710.7830.7770.775
    COVID5315160.7790.7680.774
    ShuffleNetv2NonCOVID5520140.7330.7970.7640.754
    COVID4914200.7780.7100.742
    AlexNetNonCOVID5018190.7350.7250.7300.732
    COVID5119180.7280.7390.734
    下载: 导出CSV
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  • 收稿日期:  2021-01-12
  • 网络出版日期:  2021-03-01

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