%0 Journal Article %A WU Baorong %A QIANG Yan %A WANG Sanhu %A TANG Xiaoxian %A LIU Xijing %T Fusing Multi-Dimensional Convolution Neural Network for Lung Nodules Classification %D 2019 %R 10.3778/j.issn.1002-8331.1809-0190 %J Computer Engineering and Applications %P 171-177 %V 55 %N 24 %X 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. %U http://cea.ceaj.org/EN/10.3778/j.issn.1002-8331.1809-0190