## 5 Discussion  
We quantitatively analyzed the performance of the network via experiments. The success of this method can be summarized by three main aspects. Firstly, the multi-scale nodule blocks were gradually integrated (in two different orders) so that the network could encode rich and complementary volume information according to the morphological features of the nodules and their neighboring organs. Secondly, two different branches of 3D CNN were designed for two inputs with different characteristics. The expression features can be fully extracted according to the characteristics of each input. Thirdly, the weights which were automatically determined by back propagation were used to fuse the expression features. The network can improve its performance by integrating complementary information more reasonably. Here, we discuss the differences between the 3D and 2D methods. Table 6 shows the cost of [PERSON] et al. 's [8] advanced 2D method and our 3D method. It can be seen that the 3D method takes up more memory and training time than the 2D method in model training. This is due to the fact that, during the training process, the feature map of the 3D method always has one dimension more than the 2D method, which leads to its computational complexity. Comparing the performance of [PERSON] et al. 's [8] 2D method and our 3D method on the V1 and V2 datasets, as can be seen from Tables 3 and 4, on the V1 dataset with less data, the 2D method achieved good performance at low FP/scan, and its sensitivities were 0.121, 0.082, 0.048, and 0.011 higher than the 3D method at the 0.125, 0.25, 0.5, and 1 FP/scan, respectively. On the large-sized V2 dataset, the sensitivities of the 2D method were 0.071, 0.064, and 0.011 higher than the 3D method at the 0.125, 0.25, and 0.5 FP/scan, respectively. These differences were reduced on the V2 dataset. As the data increased, \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Sensitivity** & **FP/Scan** & **0.125** & **0.25** & **0.5** & **1** & **2** & **4** & **8** & **Average CPM** \\ \hline MSH-CNN-RI & 0.716 & 0.773 & 0.831 & 0.885 & 0.900 & 0.904 & 0.936 & 0.849 \\ MSH-CNN-IC & 0.763 & 0.811 & 0.840 & 0.905 & 0.909 & 0.926 & 0.942 & 0.871 \\ MSH-CNN-AF & 0.744 & 0.782 & 0.842 & 0.873 & 0.897 & 0.915 & 0.940 & 0.856 \\ MSH-CNN-II & 0.771 & 0.818 & 0.839 & 0.885 & 0.905 & 0.917 & 0.938 & 0.868 \\ MSH-CNN-EI & 0.741 & 0.776 & 0.835 & 0.870 & 0.913 & 0.920 & 0.943 & 0.857 \\ MSH-CNN (ours) & 0.759 & 0.812 & 0.859 & 0.901 & 0.917 & 0.928 & 0.943 & 0.874 \\ MSH-CNN-LT & 0.932 & 0.944 & 0.957 & 0.976 & 0.982 & 0.989 & 0.991 & 0.967 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: CPM scores of different structure networks. MSH-CNN-RI radically integrates nodule blocks; MSH-CNN-IC interchange the inputs of two branches; MSH-CNN-AF uses addition fusion without weights; MSH-CNN-II has both branches containing inception; MSH-CNN-EI has neither branch containing Inception; MSH-CNN-LT uses the LUNA16 + Tianchi dataset. the sensitivities at the low FP/scan and the performance (CPM) of the 3D method grew faster than those of the 2D method. The reason was that, due to the complexity of the 3D network, it could not be trained adequately when the dataset was small, resulting in the sensitivity being relatively low at low FP/scan. When the dataset was large, 3D network could be fully trained, and its performance improved rapidly, eventually achieving better performance. It can be expected that, on a large-sized dataset, the 3D method has a brighter future than the 2D method. Table 7 shows the CPM scores for each round of five-fold cross-validation on the V1 dataset. Fold five with the least training data had the lowest CPM scores; the CPM of fold four with the largest training dataset and positive and negative sample distribution was relatively uniform in the training set, and the test set was the highest.