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.

Key words: lung nodule classification, convolutional neural network, deep learning, multi-dimensional, weighted fusion, CT image

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。

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