# A Computer-Aided Detection System for the Detection of Lung Nodules Based on 3D-ResNet
## 5 Conclusions  
In this paper we proposed a nodule discovery algorithm based on a three-section U-NET and a CAD system false positive reduction algorithm based on a 3D residual network. The system presented two successes. First, in terms of candidate detection, compared to the single-section U-NET used by Z-NET, we used the optimized U-NET for detection in three sections. Then, we used a high threshold to extract the candidate nodule set and finally merged the three candidate sets. In this way, the spatial information of the data can be better used to improve the detection intensity at the nodule detection stage, while the high threshold reduces the number of false positive nodules to a large extent. As shown by Table 1, at a similar detection intensity of 95.2%, the number of false positive nodules generated by this method was only 30% that of the other detection methods. Secondly, in the false positives reduction stage, to make better use of the data's spatial information, we abandoned the traditional 2D convolution neural network and used a 3D convolution neural network. Because spatial information is essential for spatial data like CT scans, using spatial information effectively can greatly improve the ability of the network to identify nodules. At the same time, we increased the depth of the network as much as possible to achieve better performance. We adopted a 3D residual network to make the deep 3D network easy to train. For each nodule, we extracted five 3D samples of different sizes into a joint judge to eliminate the effect of uneven nodule size distribution. The results show that this improved performance to some extent. Finally, we implemented a multi-section 2D convolution neural network (CUMedVis) [31] and compared it with our algorithm on the same data. The results show that our 3D residual network had better performance and a higher CPM score (0.780). Based on the experimental results, we conclude that 3D convolution neural networks have a stronger spatial feature extraction capability for 3D data with spatial information (e.g., CT scans) than 2D networks, and therefore can obtain better performance and CPM scores. In this paper, we focused on the optimization of both nodule candidate detection and false positives reduction, and achieved good results. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**CAD Systems**} & \multirow{2}{*}{**Year**} & \multirow{2}{*}{**\#Scans**} & **Slice** & **Nodule** & **Agreement** & **Nodules** & **Sensitivity (\%)** \\  & & & **Thickness** & **Size (mm)** & **Levels** & & **/FPs/Scan** \\ \hline
3D-ResNet & - & 888 & \(\geq\)2.5 & \(\geq\)3 & at least 3 & 1186 & 92.3/4.0 86.5/1.0 \\ CUMedVis [31] & 2016 & 888 & \(\geq\)2.5 & \(\geq\)3 & at least 3 & 1186 & 90.7/4.0 84.8/1.0 \\ DIAG\_CONVNET [12] & 2016 & 888 & \(\geq\)2.5 & \(\geq\)3 & at least 3 & 1186 & 90.1/4.0 85.4/1.0 \\ LungCAM [32] & 2015 & 949 & - & \(\geq\)3 & at least 3 & 1749 & 80.8/8.0 \\ [PERSON] et al. [13] & 2015 & 865 & \(\geq\)2.5 & \(\geq\)3 & at least 3 & 1147 & 76.0/4.0 73.0/1.0 \\ [PERSON] et al. [4] & 2014 & 108 & 0.5–3 & \(\geq\)4 & at least 3 & 68 & 75.0/2.0 \\ [PERSON] and [PERSON] [6] & 2013 & 58 & 0.5–3 & 3–30 & at least 1 & - & 95.3/2.3 \\ [PERSON] et al. [33] & 2013 & 360 & - & \(\geq\)3 & at least 4 & - & 83.0/4.0 \\ [PERSON] and [PERSON] [34] & 2013 & 84 & 0.5–3 & 5–20 & at least 1 & 103 & 80.0/4.2 \\ [PERSON] et al. [35] & 2012 & 84 & 1.25–3 & \(\geq\)3 & at least 1 & 148 & 97.0/6.1 88.0/2.5 \\ [PERSON] and [PERSON] [36] & 2012 & 85 & 1.25–3 & \(\geq\)3 & at least 3 & 111 & 80.0/7.4 75.0/2.8 \\
3D MixNet [10] & 2019 & 89 & 1.25–3 & \(\geq\)3 & at least 3 & 127 & 88.83/3.5 \\ [PERSON] et al. [37] & 2018 & 85 & 1.25–3 & \(\geq\)3 & at least 3 & 119 & 86.0/2.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: CAD system using the Lung Image Database Consortium (LIDC-IDRI) dataset published in recent years. **Author Contributions:** Conceptualization, J.N. ; methodology, J.N. and H.Z. ; software, L.L. ; validation, J.N., H.Z., L.L., and P.S.