# A Multi-Site Anti-Interference Neural Network for ASD Classification
## 3 Results  
### Anti-Interference Classification Results of Multi-Site Data  
CNN (1D) was used as the representation learning module under the ten-fold cross-verification of the whole site to compare the classification performance of norm neural networks and MS-AINN. Evaluation indicators include classification accuracy, AUC, precision, recall, f1 and site classification accuracy, and the results are shown in Table 2. The first five indexes in the table evaluated the classification performance of ASD diseases of the model. In addition, the classification accuracy of sites was obtained by training the features learned from representations separately, which was used to evaluate the degree of confounding of features by sites. CNN (1D) in the table refers to the classification training conducted by the representation learning module directly after extracting features.  
It can be seen from the table that the classification performance of MS-AINN-CNN (1D) is significantly improved compared with CNN (1D). Accuracy increased by 2.79%, AUC increased by 1.14%, precision increased by 3.01%, recall increased by 2.87% and f1 increased by 2.88%. Meanwhile, the accuracy of site classification decreased by 5.63%.  
In order to prove that the classification performance improvement of MS-AINN is due to the neural network structure rather than the representation learning part, this experiment also replaced the representation learning module with Multilayer perceptron (MLP) and CNN (2D) for testing, and the results are shown in Tables 3 and 4. The input of MLP is the same as that of AE, which is a flat one-dimensional feature vector of functional connection. Specifically, a double hidden layer design is adopted. The weight form of the first layer is 19,900 \(\times\) 512, and the weight form of the second layer is 512 \(\times\) 256. CNN (2D) replaces the convolution kernel of CNN (1D) with the traditional two-dimensional convolution, with the specific forms of 32@5 \(\times\) 5 and 64@2 \(\times\) 2, and the input is the same as CNN (1D).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **ACC\%** & **AUC\%** & **Precision \%** & **Recall \%** & **f1\%** & **Site Classification ACC \%** \\ \hline CNN (1D) & 72.77 & 77.85 & 73.59 & 72.49 & 72.32 & 21.39 \\ MS-AINN-CNN (1D) & 75.56 & 78.99 & 76.60 & 75.36 & 75.20 & 15.76 \\ \hline \hline \end{tabular}
\end{table}
Table 2: CNN (1D) anti-interference classification results.  
Compared with MLP, the accuracy of MS-AINN-MLP increased by 1.97%, AUC increased by 0.81%, precision increased by 1.65%, recall increased by 1.91% and f1 increased by 2.15%. Meanwhile, the accuracy of site classification decreased by 18.31%. Compared with CNN (2D), the accuracy of MS-AINN-CNN (2D) increased by 2.75%, AUC increased by 0.93%, precision increased by 1.32%, recall increased by 2.96% and f1 increased by 3.37%. Meanwhile, the accuracy of site classification decreased by 18.96%.  
We also made comparisons with previous ASD classification methods that used data from the same 17 sites as ours for ASD classification in the ABIDE multi-site dataset. The results are shown in Table 5, and it can be seen that the proposed method achieves the optimal classification accuracy.