## 4 Results
### Classification Experiment of Training on Combining Site Data and Test on Benchmark Test Data  
The ADHD classification experiment of training on combining site data and test on benchmark test data, combining site experiment for short, refers to combining the training data of all five imaging sites in Table 1 as the training set to train the model and then taking the benchmark test data set of each imaging site used in the competition of ADHD-200 as the test set for the classification experiment. For example, in the NYU classification of the combining site experiment, there were 619 training samples, which were obtained by combining the training data of these five imaging sites (i.e., KKI, NI, NYU, OHSU, and Peking) shown in Table 1, and there were 41 test samples, which were the benchmark test set of the NYU imaging site. In the combining site experiment, the weight of the cross-loss function \(\alpha\) was set to 2. The ADHD classification results on the combining site experiment of different methods are displayed in Table 4, which shows that the proposed method performed well in general compared to the most advanced methods, especially in the case of the OHSU data set, where the recognition accuracy was much higher than the other methods and also achieved an average accuracy of 72.02%, a state-of-the-art performance.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Evaluation** & **Methods** & **KKI** & **NI** & **NYU** & **OHSU** & **Peking** \\ \hline \multirow{4}{*}{Specificity} & [PERSON] et al. (2014) [1] & 62.5 & 64.29 & - & 89.29 & 92.59 \\  & Fusion of fMRI and non-imaging & 75.0 & 42.8 & 41.6 & - & 92.6 \\  & DeepFMRI (2020) [15] & - & 71.4 & 91.6 & - & 79.1 \\  & Our proposed method & 87.5 & 71.43 & 75 & 92.86 & 59.26 \\ \hline \multirow{4}{*}{Sensitivity} & [PERSON] et al. (2014) [1] & 33.33 & 27.27 & - & 50.00 & 20.83 \\  & Fusion of fMRI and non-imaging & 100 & 45.4 & 68.9 & - & 33.3 \\  & DeepFMRI (2020) [15] & - & 63.6 & 65.5 & - & 48.1 \\  & Our proposed method & 66.67 & 54.55 & 82.76 & 33.33 & 60.87 \\ \hline \multirow{4}{*}{Accuracy} & [PERSON] et al. (2014) [1] & 54.55 & 48 & - & 82.35 & 58.82 \\  & Fusion of fMRI and non-imaging & 81.8 & 44 & 60.9 & - & 64.7 \\ \cline{1-1}  & DeepFMRI (2020) [15] & - & 67.9 & 73.1 & - & 62.7 \\ \cline{1-1}  & Our proposed method & 81.82 & 64 & 80.49 & 82.35 & 60 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The specificity (%), sensitivity (%), and accuracy (%) comparison of different methods in the individual site experiments.  
The specificity, sensitivity, and accuracy of the proposed method in the combining site experiment are shown in Table 5. Increasing the training samples, the sensitivities of NI and Peking were significantly improved compared to the individual site experiments. At the same time, it can also be observed that the imbalance of the test sets in KKI and OHSU was severe, and the number of ADHD samples in these test sets was small, which resulted in the low sensitivity of KKI and OHSU in the combining site experiment. The confusion matrices of the classification results in the combining site experiment are shown in Figure 5.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **KKI** & **NI** & **NYU** & **OHSU** & **Peking** & **Average** \\ \hline ADHD-200 (2012) [23] & 61.9 & 56.95 & 35.19 & 65.37 & 51.05 & 54.09 \\ Fusion of fMRI and non-imaging data (2018) [7] & 81.8 & - & 56.1 & - & 60.7 & 66.2 \\ DeepFMRI (2020) [15] & - & 60 & 65.8 & - & 43.1 & 56.3 \\ SC-CNN-Attention (2020) [16] & 77.7 & 75.3 & 60.4 & 64.4 & 65.2 & 68.6 \\ Our proposed method & 81.82 & 68 & 70.73 & 73.53 & 66 & 72.02 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Classification accuracies (%) comparison of the different methods in the combining site experiment.  
Figure 4: The confusion matrices of the classification results in the individual site experiments: (**a**) KKI data set; (**b**) NI data set; (**c**) NYU data set; (**d**) OHSU data set; (**e**) Peking data set.