# Voice Pathology Detection and Classification Using Convolutional Neural Network Model
## 4 Experimental Results  
In this work, we propose a deep learning Convolutional Neural Network (CNN) model to perform pathology detection based on numerical analysis of voice signals. It is implemented in a Voice Pathology Detection DeepNet system using Python programming language. It is trained and tested by using a Google Colaboratory server. The testing computer has a 69 K GPU graphics card and 8 GB of RAM. It runs the Windows 10.1 operating system. The model architecture includes four fully connected layers. The CNN model of the Voice Pathology Detection DeepNet system is tested using the Saarbruecken Voice Database (SVD) dataset [1]. The dataset contains recordings of 71 types of voice signals in which the signals were split into 64 ms long Hamming windowed segments with 30 ms overlap. It is arranged as a sequence of time-based vectors. Each vector as labelled as healthy or pathological class. Table 3 presents the training confusion matrix of the SVD dataset. The table shows that the 300 training samples are divided equally to 150 healthy and 150 pathological classes to ensure a balanced training process. The CNN model is able to achieve an average prediction score of 93.72% accuracy, 94.11% sensitivity or recall and 95.41% specificity. Table 4 shows the results of precision, f1-score and recall for healthy and pathological classes in the training phase. Table 5 presents the testing confusion matrix to the SVD dataset. The table shows that the 1074 testing instances are divided into 200 healthy and 874 pathological classes to insure robust testing process. In the testing phase, the CNN model is able to achieve an average prediction score of 96.11% accuracy, 95.38% sensitivity or recall and 95.97% specificity. Table 6 shows the results of precision, f1-score and recall for healthy and pathological classes in the testing phase. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Class** & **Precision** & **f1-Score** & **Recall** \\ \hline Pathological & 94.54 & 93.98 & 95 \\ \hline Healthy & 93.46 & 92.02 & 94 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The classification report of the training phase. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Class** & **True P** & **True H** & **No. of Samples** \\ \hline Pred: pathological & 139 & 11 & 150 \\ \hline Pred: healthy & 9 & 141 & 150 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The confusion matrix of the training phase. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Class** & **True P** & **True H** & **No. of Samples** \\ \hline Pred: pathological & 839 & 35 & 874 \\ \hline Pred: healthy & 11 & 189 & 200 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The confusion matrix of the testing phase. Based on the training methodology that is mentioned early, the setting of the prediction model includes 0.02 learning rate, 2 batch size, 10 number of epochs, and 0.93 momentum as presented in Table 2. Table 7 shows the training Train Loss and testing Valid Loss for each epoch along with the accuracy and time per epoch results. From the observation of the results, during the progress of the Epoch, the Train Loss and testing Valid Loss are slightly increased, the accuracy result is slightly increased and the time per epoch is almost constant at 02:54 s. This result can be attributed to the ResNet34 training algorithm's ability to rapidly adapt to the discriminative features of the SVD dataset and provide significant generalization. To further analyze the behaviour of the model, we investigated the relationship between the loss and learning rate. The analysis result shows that the best order of magnetite falls in the middle of the learning rate when the loss have values between 0.46 and 0.48 (i.e., between log scales le-04 and le-03) as shown in Figure 3. Hence, the centroid of this area is selected to set the learning rate of the ResNet34 algorithm.