## 2 Related Work  
In early tumor imaging research, researchers mainly focused on using traditional computer vision methods to artificially design expression features according to the appearance of nodules. For example, [PERSON] et al. [20] designed an algorithm for segmentally identifying nodules based on the shape, position, brightness, and gradient characteristics of candidate nodules. [PERSON] et al. [21] extracted 128 features based on shape, brightness, texture, and neighboring organ information of the nodule. The method used six classifiers to test and compared the results: support vector machine using radial basis function kernel (SVM-RBF), k-nearest neighbor, GentleBoost, random forest, recent mean, and linear classifiers. The sensitivity of the GentleBoost classifier reached 80% at 1.0 FP/scan. Although this method achieved good results, only pulmonary nodules with certain characteristics (e.g., shape, size, and texture) were found. However, these surface features lacked discrimination because the shape, size, and texture of pulmonary nodules vary widely, making it difficult to distinguish between real nodules and false-positive nodules. Recently, deep neural networks became more common than manual design features. By studying deep neural network applications to reduce false-positive pulmonary nodules, we can find characteristic patterns. The use of 3D volumetric information, multi-scale nodule block input, and multi-branch network parallelization became more and more popular, and achieved better and better results. In using 3D volumetric information, researchers gradually shifted away from using multi-view slice information of nodules to volumetric information of nodules. For example, [PERSON] et al. [16] proposed the concept called 2.5D, which extracted three orthogonal slices for each candidate nodule, and used convolutional neural networks to extract expression features for classification. [PERSON] et al. [5] proposed a 2D multi-view convolutional neural network that extracted nine slices of different views for each nodule, inputting them into convolutional neural networks to extract expression features, and finally merging these features by certain methods. In the LIDC-IDRI [22] dataset test, this method achieved good sensitivity, but still had some defects when the false-positive rate was low. Therefore,Ding et al. [17] proposed to extract 3D nodule blocks as the input of 3D-CNN according to the location of candidate nodules. In the multi-scale nodule block inputting process, [PERSON] et al. [18] proposed a multi-scale CNN, i.e., MCNN, to extract blocks (of different scales) according to candidate nodule coordinates. All the blocks were merged and a convolutional neural network was used to extract expression features for classification. [PERSON] et al. [8] proposed multi-scale gradual integration CNN, using 2D convolution kernels to gradually fuse nodule blocks of different scales, and two branches were input to extract expression features. Finally, the nodules were classified by fusing these expression features. Multi-scale blocks have several advantages over single blocks; the advantages are as follows: (1) multi-scale blocks can completely segment nodules with large diameters; (2) multi-scale blocks can ensure the smaller-diameter nodules maintain a larger proportion of the block; (3) multi-scale blocks can increase the neighboring organ information of the nodule and bring abundant volumetric information while ensuring the integrity of nodule information. In multi-branch network parallelization, [PERSON] et al. [10] extracted nodule blocks of three scales and input them into three parallel branches for classification. Finally, the classification results were fused by the weights determined manually. When [PERSON] et al. [8] fused the two parallel branches, a simple addition method was used to fuse them. Compared to a single branch network, multi-branch networks can extract more abundant complementary expression features.