## 4 Experiments and Results
### Experimental Results  
We used several models from References (20; 29; 10; 29) as baselines. [PERSON] et al. (2010) was based on a 3D method, where they used three sizes of nodule blocks as inputs. UACNN (2010) selected three slices of the candidate nodule center, 3 mm above and 3 mm below, as inputs. [PERSON] et al. (2015) extracted nine different views of the nodules as input based on a 2D method. [PERSON] et al. (2019) eliminated prediction inconsistencies by raising the threshold at each iteration. [PERSON] et al. (2018) gradually integrated nodule blocks of three sizes as inputs, based on 2D convolution kernels. Tables 3 and 4 summarize the CPM scores of these different methods on V1 and V2 datasets. Table 3 summarizes the best performance of our MSH-CNN algorithm on the V1 dataset. The proposed MSH-CNN was superior to most methods in terms of average CPM. Compared to [PERSON] et al. (2010), UACNN, and [PERSON] et al. (2015) in terms of average CPM, our 3D network had increases by 0.047, 0.050, and 0.036, respectively. Our method was 0.001 lower than [PERSON]'s method (29) at the 0.125 FP/scan, but it had a higher average CPM and sensitivity at the 0.25, 0.5, 1, 2, 4, and 8 FP/scan. In comparison with [PERSON] et al. (2018), although the sensitivity of our method was lower when the FP/scan was less than 1, our network still achieved the best performance at the 2, 4, and 8 FP/scan. Our method outperformed most methods in the false-positive rates of range 1-4 which are commonly used in clinical applications ([PERSON] et al., 2018). Figure 3 shows some examples of correctly classified candidate nodules. The FROC curves of different methods are shown in Figure 4. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Sensitivity**} & \multicolumn{1}{c}{**FP/Scan**} & \multicolumn{1}{c}{**0.125**} & \multicolumn{1}{c}{**0.25**} & \multicolumn{1}{c}{**0.5**} & \multicolumn{1}{c}{**1**} & \multicolumn{1}{c}{**2**} & \multicolumn{1}{c}{**4**} & \multicolumn{1}{c}{**8**} & \multicolumn{1}{c}{**Average CPM**} \\ \hline \multirow{3}{*}{**V2**} & [PERSON] et al. (2015) & 0.669 & 0.760 & 0.831 & 0.892 & 0.923 & 0.944 & 0.960 & 0.854 \\  & [PERSON] et al. (2018) & 0.904 & 0.931 & 0.943 & 0.947 & 0.952 & 0.956 & 0.962 & 0.942 \\  & MSH-CNN (ours) & 0.833 & 0.867 & 0.932 & 0.953 & 0.960 & 0.977 & 0.984 & 0.929 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: CPM scores of different methods on V2 dataset of LUNA16. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Dataset**} & \multicolumn{2}{c}{**Fold 1**} & \multicolumn{2}{c}{**Fold 2**} & \multicolumn{2}{c}{**Fold 3**} & \multicolumn{2}{c}{**Fold 4**} & \multicolumn{2}{c}{**Fold 5**} \\ \cline{3-11}  & \multicolumn{2}{c}{**Training**} & **Test** & **Training** & **Test** & **Training** & **Test** & **Training** & **Test** & **Training** & **Test** \\ \hline \multirow{3}{*}{V1} & Patient & 533 & 133 & 533 & 133 & 533 & 133 & 533 & 133 & 532 & 134 \\  & Nodule & 936 & 184 & 867 & 253 & 910 & 210 & 912 & 208 & 855 & 265 \\  & Augmentation & 101,088 & - & 93,636 & - & 191,916 & - & 98,496 & - & 92,340 & - \\  & Non-nodule & 439,022 & 110,692 & 440,811 & 108,903 & 438,087 & 111,627 & 444,860 & 104,854 & 436,076 & 113,638 \\ \hline \multirow{3}{*}{V2} & Patient & 711 & 177 & 711 & 177 & 710 & 178 & 710 & 178 & 710 & 178 \\  & Nodule & 970 & 196 & 953 & 213 & 917 & 249 & 930 & 236 & 894 & 272 \\  & Augmentation & 100,440 & - & 102,924 & - & 99,036 & - & 104,760 & - & 96,552 & - \\  & Non-nodule & 598,891 & 154,527 & 602,299 & 151,119 & 605,820 & 147,598 & 606,027 & 147,391 & 600,635 & 152,783 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Statistics of the dataset used for five-fold cross-validation.