## 4 Results  
To evaluate the performance of the proposed system, the following parameters have been used [61; 62]. _Accuracy_ is simply a ratio of the correctly predicted observations to the total observations, as defined by:  
\[Accuracy=\frac{TP+TN}{(TP+FP+FN+TN)} \tag{3}\]  
_Precision_ is the ratio of correctly predicted positive observations to the total predicted positive observations. The precision is defined by:  
\[Precision=\frac{TP}{(TP+FP)} \tag{4}\]  
_Recall_ is the ratio of correctly predicted positive observations to all observations in the actual class. It is formulated by:  
\[Recall=\frac{TP}{(TP+FN)} \tag{5}\]  
_F1 score_ is the weighted average of _precision_ and _recall_. Therefore, this score takes both false positives and false negatives into account. The _F1 score_ is defined by:  
\[F1=\frac{2*Recall*Precision}{Recall+Precision} \tag{6}\]  
_NPV_ defines the fraction of the tests that correctly detect healthy individuals. The _NPV_ is defined by:  
\[NPV=\frac{TN}{(TN+FN)} \tag{7}\]To investigate the performance of the proposed system, 10 simulations were conducted using the first investigated model consisting of bandpass filters. First, the CNN was trained with 100 control and 100 pathological samples. Five-fold cross-validation was used to ensure the accuracy of the training. The simulations were run for enough epochs to achieve a training accuracy of 100%. Once trained, the 100 other control samples and 100 pathological samples were used to test the network's performance. The training, validation, and testing results of the proposed algorithm for the first model are listed in Table 3. The table shows that the proposed system's average training, validation, and testing accuracies are 100%, 85.96%, and 77.91%, respectively. The testing performances of the proposed system in terms of _TPF_, _TNF_, _FPF_, and _FNF_ are listed in Table 4 and the corresponding classification matrix is shown in Table 5. Based on the data presented in Table 5, it can be concluded that the proposed system can correctly detect pathological voices, resulting an accuracy of 76.67% with the first model. On the other hand, the system can detect control (i.e., normal) voices with an accuracy of 79.17%. Ten more simulations were conducted using the second model consisting of the gammatone filters with the same set of control and pathological samples that were used in the previous simulations. The proposed algorithm's training, validation, and testing results \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Simulation** & \multicolumn{3}{c}{_Accuracy (\%)_} \\ \cline{2-4}
**No.** & **Training** & **Validation** & **Testing** \\ \hline
1 & 100 & 88.00 & 79.17 \\
2 & 100 & 85.83 & 79.17 \\
3 & 100 & 88.83 & 75.00 \\
4 & 100 & 85.83 & 79.17 \\
5 & 100 & 87.83 & 83.33 \\
6 & 100 & 88.00 & 79.17 \\
7 & 100 & 85.83 & 75.00 \\
8 & 100 & 83.33 & 75.00 \\
9 & 100 & 85.33 & 79.17 \\
10 & 100 & 80.83 & 75.00 \\ Average & 100 & 85.96 & 77.91 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Training and testing accuracies with bandpass filters. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Simulation** & \multicolumn{3}{c}{_TPF_} & \multicolumn{1}{c}{_TNF_} & \multicolumn{1}{c}{_FPF_} & \multicolumn{1}{c}{_FNF_} \\
**No.** & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline
1 & 83.33 & 75.00 & 25.00 & 16.67 \\
2 & 83.33 & 75.00 & 25.00 & 16.67 \\
3 & 75.00 & 75.00 & 25.00 & 25.00 \\
4 & 75.00 & 83.33 & 16.67 & 25.00 \\
5 & 75.00 & 91.67 & 8.33 & 25.00 \\
6 & 75.00 & 83.33 & 16.67 & 25.00 \\
7 & 66.67 & 83.33 & 16.67 & 33.33 \\
8 & 75.00 & 75.00 & 25.00 & 25.00 \\
9 & 83.33 & 75.00 & 25.00 & 16.67 \\
10 & 75.00 & 75.00 & 25.00 & 25.00 \\ Average & 76.67 & 79.17 & 20.83 & 23.33 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Simulation results with bandpass filters.