## 4 Experiments and Results
### Attention Deficit Hyperactivity Disorder Diagnosis
#### 4.1.2 Accuracy and Analysis of the Network's Depth  
It is well known that the depth of a network has a big impact on its performance. Table 3 shows the accuracy and OD score of networks with different depth configurations. For the baseline method (\(A_{n}\)), we can see that \(A_{1}\) achieved the best result. However, its performance would deteriorate as we deepened the networks. We believe that the aggravation of overfitting was responsible for this degradation since the number of parameters would increase dramatically with a deeper network. As shown in the third column of Table 3, from \(A_{0}\) to \(A_{6}\), the number of parameters increased from 4.8 M to 60.6 M. As the number of tunable parameters increased, the models tended to be more susceptible to overfitting, resulting in the severe deterioration of the OD score, as well as the performance. While in \(B_{n}\) and \(C_{n}\), since the 3D convolutions were replaced by parameter efficient 3D-DSC modules, considerable parameters could be reduced, and more parameter space was made available for designing deeper networks. Note that \(B_{4}\) and \(A_{1}\) have a similar number of parameters, but the effective depth of \(B_{4}\) increased from 11 layers to 20 layers; thus, more representation power could be expected. In Table 3, a stable accuracy improvement of \(B_{1}\) to \(B_{5}\) can be observed. In addition, we can notice that the OD scores of \(B_{4}\) and \(B_{5}\) were both smaller than \(A_{1}\), which further validated that our method could better handle the risk of overfitting. Another example that could verify the performance of our method was \(C_{6}\). We can see that \(B_{5}\) achieved the best performance in \(B_{n}\) networks, and the performance and OD score of \(B_{6}\) already showed the trend of degradation. As shown in Table 2, \(C_{6}\) replaced one more layer with 3D-DSC than \(B_{6}\) (between the first two pooling layers); therefore, \(C_{6}\) had the same depth as \(B_{6}\), but it had less parameters and a stronger ability to avoid overfitting. We can see that \(C_{6}\) achieved the highest accuracy among all networks, and its OD score was smaller than \(B_{6}\). Although deeper networks might provide better performance, we could not continue to deepen the network due to the limit of GPU memory (12 GB of Titan-XP). These results confirmed that deeper networks could be obtained and effectively trained with 3D-DSC, thus improving the network's representatio \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Network** & **Depth** & **Params** & **Accuracy** & **OD** \\ \hline \(A_{0}\) & 8 & 4.8 M & 73.17\% & 0.2785 \\ \(A_{1}\) & 11 & 14.1 M & 74.89\% & 0.2806 \\ \(A_{2}\) & 14 & 23.4 M & 74.53\% & 0.2807 \\ \(A_{3}\) & 17 & 32.7 M & 71.94\% & 0.2961 \\ \(A_{4}\) & 20 & 42.0 M & 70.78\% & 0.3368 \\ \(A_{5}\) & 23 & 51.3 M & 69.91\% & 0.3390 \\ \(A_{6}\) & 26 & 60.6 M & 69.22\% & 0.3567 \\ \(B_{1}\) & 11 & 7.2 M & 73.45\% & 0.2415 \\ \(B_{2}\) & 14 & 9.6 M & 74.58\% & 0.2498 \\ \(B_{3}\) & 17 & 12.1 M & 75.22\% & 0.2526 \\ \(B_{4}\) & 20 & 14.4 M & 75.57\% & 0.2610 \\ \(B_{5}\) & 23 & 16.9 M & 75.79\% & 0.2689 \\ \(B_{6}\) & 26 & 19.3 M & 75.74\% & 0.2809 \\ \(C_{6}\) & 26 & 19.2 M & 76.70\% & 0.2580 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance comparison based on 5 fold cross-validation. \(A_{0}\)\(\sim\)\(A_{6}\) are normal 3D CNNs with different depths. \(B_{1}\)\(\sim\)\(B_{6}\) and \(C_{6}\) are the separated 3D CNNs with 3D-DSC. OD denotes the Overfitting Distance. The batch size is 4 in this experiment. shown in Table 4, compared with several state-of-the-art methods attempting to assist the diagnosis, \(C_{6}\) outperformed the others by a large margin on the ADHD-200 even if only a single modality of the dataset was used in our method. We attributed the performance improvement of the proposed 3D-DSC based methods to the following two main factors. (1) The parameter efficient nature of the proposed 3D-DSC made the effective training of deeper network possible while reducing the risk of overfitting.