| 融合多维度卷积神经网络的肺结节分类方法 |
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| 引用本文： | 吴保荣,强彦,王三虎,唐笑先,刘希靖.融合多维度卷积神经网络的肺结节分类方法[J].计算机工程与应用,2019,55(24):171-177. |
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| 作者姓名： | 吴保荣 强彦 王三虎 唐笑先 刘希靖 |
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| 作者单位： | 太原理工大学 信息与计算机学院,山西 晋中,030600;吕梁学院 计算机科学与技术系,山西 吕梁,033000;山西省人民医院 PET/CT中心,太原,030024;山西农业大学 软件学院,山西 晋中,030600 |
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| 基金项目： | 国家自然科学基金;虚拟现实技术与系统国家重点实验室开放基金;虚拟现实技术与系统国家重点实验室开放基金;山西省回国留学人员科研项目 |
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| 摘 要： | 针对CT图像肺结节分类任务中分类精度低，假阳性高的问题，提出了一种加权融合多维度卷积神经网络的肺结节分类模型，该模型包含两个子模型：基于二维图像的多尺度密集卷积网络模型，以捕获更宽泛的结节变化特征并促进特征重用；基于三维图像的三维卷积神经网络模型，以充分利用结节空间上下文信息。使用二维和三维CT图像训练子模型，根据子模型分类误差计算其权重，对子模型分类结果进行加权融合，得到最终分类结果。该模型在公共数据集LIDC-IDRI上分类准确率达到94.25%，AUC值达到98%。实验结果表明，加权融合多维度模型可以有效地提升肺结节分类性能。
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| 关 键 词： | 肺结节分类 卷积神经网络 深度学习 多维度 加权融合 CT图像 |
Fusing Multi-Dimensional Convolution Neural Network for Lung Nodules Classification |
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| WU Baorong,QIANG Yan,WANG Sanhu,TANG Xiaoxian,LIU Xijing.Fusing Multi-Dimensional Convolution Neural Network for Lung Nodules Classification[J].Computer Engineering and Applications,2019,55(24):171-177. |
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| Authors: | WU Baorong QIANG Yan WANG Sanhu TANG Xiaoxian LIU Xijing |
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| Affiliation: | 1.College of Information and Computer, Taiyuan University of Technology, Jinzhong, Shanxi 030600, China
2.College of Computer Science and Technology, Lvliang University, Lvliang, Shanxi 033000, China
3.Department of PET/CT Center, Shanxi Provincial People’s Hospital, Taiyuan 030024, China
4.College of Software, Shanxi Agricultural University, Jinzhong, Shanxi 030600, China |
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| Abstract: | In order to solve the problem of low classification precision and high false positive in the classification task of lung nodules in CT image, a benign and malignant classification model of lung nodules based on weighted fusion multi-dimensional convolution neural network is proposed. The model contains two sub-models：a multi-scale dense convolutional network model based on two-dimensional images to capture more extensive nodule variation features and promote feature reuse, and the three-dimensional convolutional neural network model based on three-dimensional images to make full use of spatial context information of nodules. 2D and 3D CT images are used to train the sub-models. The weights of the sub-models are calculated according to the classification errors, and then the weights are used to fuse the sub-models classification results. The more accurate classification results are obtained. The classification accuracy of the model is 94.25% and the AUC value is 98% on the public dataset LIDC-IDRI. The experimental results show that the weighted fusion multi-dimensional model can effectively improve the classification performance of lung nodules. |
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| Keywords: | lung nodule classification convolutional neural network deep learning multi-dimensional weighted fusion CT image |
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