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

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