## 3 Experimental Results
### Transfer Learning  
We selected the best method identified in Table 2 for healthy data and conducted the transfer learning approaches on this method to data from unhealthy patients. The results in Table 3 show the recorded accuracy values for several experiments on the proposed CNN \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Classification Techniques** & **Healthy Data** \\ \hline
1 & Proposed CNN & 0.86 \(\pm\) 0.01 \\ \hline
2 & Random Forest & 0.82 \(\pm\) 0.01 \\ \hline
3 & Feed forward NN & 0.84 \(\pm\) 0.02 \\ \hline
4 & Naive Bayesian classifier & 0.45 \(\pm\) 0.02 \\ \hline
5 & K-Nearest neighbors & 0.83 \(\pm\) 0.02 \\ \hline
6 & Support vector machine & 0.83 \(\pm\) 0.01 \\ \hline
7 & Classification tree & 0.64 \(\pm\) 0.06 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of classification techniques with Healthy subjects’ data. Figure 6: Comparison between the synthetic and an original mask across several channels. model. Each defines the data used for training and testing with their respective data sizes. It has to be mentioned that the trained model never sees the testing data, neither during the training process nor the hyper-parameters' tuning process. Several baseline experiments were conducted to assess the other added value of transfer learning approaches. First, we trained on healthy control data and tested on healthy control. This experiment provided an upper bound of performance with the highest accuracy of 86%. This high score is possibly also due to the expected higher homogeneity of healthy control. The same experiment was carried out while training unhealthy and testing unhealthy patients. A drop of about 10% of accuracy was observed, which builds a second baseline with fewer patients. The investigated transfer learning approaches were expected to provide performances between these two bounds. We considered four transfer learning strategies for this experiment: (i) brute transfer (training on healthy and testing on unhealthy data), (ii) mixed transfer (adding some unhealthy data to healthy data to train the model), (iii) weight transfer (fine-tuning on unhealthy data) and (iv) transfer learning with data augmentation. On the brute transfer strategy, as indicated in Table 3 row 3, we trained our model with 81 healthy control subjects and conducted testing on all 55 unhealthy patients. We recorded an average accuracy of \(0.74\pm 0.01\) for all test data size ranges. The brute transfer is therefore not bringing any improvement here. For the mix transfer strategy, Table 3 row 4, we trained our model with 81 healthy control subjects and 45 unhealthy patients. At the same time, we performed our model test with ten unhealthy patients. An improvement in accuracy to \(0.77\pm 0.01\) on test data was observed by comparison with the brute transfer. The addition of data helps, even with a mixture of healthy and unhealthy patients by comparison with pure unhealthy patients experiment of row 1. However, we do not reach the upper bound performance of row 1 despite having more data than in this experiment. This performance demonstrates a discrepancy between healthy and unhealthy patients. Figure 7 shows the validation accuracy (from validation data) of the trained model on healthy data for various amounts of added unhealthy patients \((10,20,30,45)\). We recorded a \(\cong\)1% increase in validation accuracy for every ten unhealthy patient data added to training data (seven functional network images per patient). As the third transfer learning strategy, in Table 3 row 5, we transferred the weight and bias of a model fully trained on healthy data (model of row 1) to a model for training on unhealthy data. The model was retrained and fine-tuned on 45 unhealthy patients and tested on the 10 remaining patients. A performance of \(0.78\pm 0.01\) is obtained on unhealthy test data. This result is the highest performance