## 5 Discussion
This paper mainly focused on improving FP reduction rather than developing a whole pulmonary nodule detection system which typically consists of a candidate detection component and an FP reduction component. It is a candidate screening method, and it is essentially a classification problem. This means that our method can combine with existing nodule candidate detection elements to complete the task of pulmonary nodule detection, and also can be combined with other candidate detectors to solve candidate screening problems. Our method can also be used as a classification network alone to solve classification problems of 3D data. In this paper, our evaluation standard was CPM score, which was extracted from the FRCO curve. Specifically, we measured the sensitivity at seven false-positive rates (1/8, 1/4, 1/2, 1, 2, 4, and 8 FPs per scan) and calculated their average. This performance metric was introduced by [PERSON] et al. [30]. Obviously, a perfect system has a score of 1 and the lowest score is 0. Currently, in clinical applications, the internal thresholds of most CAD systems are set to one, two, or four false positives per scan on average [30]. To make this task more challenging, the false-positive rates we used in the assessment were lower than the false-positive rates used in clinical practice. Although CNN methods are becoming more prevalent in the field of medical image analysis, most of the work in the field of medical image analysis is still based on 2D CNN. On the other hand, 3D CNN is used less because there is a lack of 3D datasets, which is further complicated by expensive doctor labeling and privacy concerns. Also, 3D CNN training costs much more than 2D CNN. Therefore, we need to increase the computing power to use 3D CNN. The network achieved good results, but there are still potential limitations, including high training costs, and the finding that the sensitivity at a low false-positive rate was slightly lower when the dataset was relatively small. Our future work will focus on extending the 3D dataset, for example, using generative adversarial networks (GANs) to generate datasets. We will also focus on combining 2D CNN and 3D CNN to reduce computing resources. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & \begin{tabular}{c} **Convolution** \\ **Kernel Type** \\ \end{tabular} & \begin{tabular}{c} **Memory Usage** \\ **(GPU)** \\ \end{tabular} & \
\begin{tabular}{c} **Training Time** \\ **(One Sample)** \\ \end{tabular} &
\begin{tabular}{c} **Testing Time** \\ **(One Sample)** \\ \end{tabular} \\ \hline [PERSON] et al. [8] & 2D & 2089 MB & About 12 h & About 0.6 s \\ MSH-CNN (ours) & 3D & 6783 MB & About 30.5 h & About 1 s \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The cost of two-dimensional (2D) and 3D networks. GPU—graphics processing unit. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Index** & **Fold 1** & **Fold 2** & **Fold 3** & **Fold 4** & **Fold 5** \\ \hline Ratio (training) & 1:469 & 1:508 & 1:481 & 1:488 & 1:510 \\ Ratio (test) & 1:602 & 1:430 & 1:532 & 1:504 & 1:429 \\ Average CPM & 0.873 & 0.874 & 0.876 & 0.878 & 0.869 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: CPM scores for each round of five-fold cross-validation on the V1 dataset. The ratio represents the ratio of nodule to non-nodule in the training/test dataset.