关键词: 计算机辅助诊断, 肺结节分类, 卷积神经网络, 多视图融合, 多尺度多模式图像

Abstract: This paper discusses the effects of pulmonary nodule images of different scales and different modes on deep convolutional neural network model. A novel 2D Multi-View Fusion（2D MVF） method is proposed for lung CT image processing which can catch more lung nodule information than the conventional 2D manner and introduce less interfering tissues than the 3D manner. To validate the model, the LIDC-IDRI and the LUNA16 datasets are proposed and four types of lung nodule images are gotten, including 2D, 3D, 2D FVF（Full-view Fusion）, and 2D MVF lung nodule images, and each type of the image contains 16, 25, 36 three different scales. Then, four types of models of 2D CNN, 3D CNN, 2D FVF-CNN, 2D MVF-CNN are constructed. The above data are used to train and verify the model. The experimental results show that 2D MVF mode has better classification performance than other lung nodule image mode，and the classification performance at small scale is relatively better comparing various nodule image scales.

Key words: computer-aided diagnosis, lung nodule classification, convolutional neural network；multi-view fusion；multi-scale and multi-mode image