## 3 Results
### Convolutional Neural Network Model for Urea Content Classification  
Similar to traditional machine learning models, the classification performance of CNN models is contingent upon both the type of input feature vectors and the measurement position. Table 7 presents the accuracy of the optimally tuned CNN models when being evaluated on the test sets according to measurement positions and feature types. It can be seen that the CNN classifier gained the highest accuracy of 83.9% when \"Skin, stomach\" was used as the measurement position and \"prep + der1\" was chosen as the feature vector. For the position-independent experiment, it attained the highest accuracy of 72.3% when the feature type \"der1\" was selected.  
In order to comprehensively assess the efficacy of the proposed CNN model described in Section 2.8 (i.e., the one which achieved the highest classification accuracy and marked with an asterisk in Table 7) in addressing the binary classification problem with imbalanced data, the following metrics were employed as evaluation measures on the test set:  
* Accuracy = \(\frac{TP+TN}{TP+TP+TN+FN}\) = 83.9%
* Recall = \(\frac{TP}{TP+FN}\) = 83.9%
* Specificity = \(\frac{TN}{TN+FP}\) = 83.9% where:
* _TP_ (True Positive): The total number of samples where the model correctly predicts the positive class, i.e., when the actual class is Unsafe, and the model also predicts it as Unsafe.
* _TN_ (True Negative): The total number of samples where the model correctly predicts the negative class, i.e., when the actual class is Safe, and the model also predicts it as Safe.
* _FP_ (False Positive): The total number of samples where the model predicts the positive class incorrectly, i.e., the actual class is Safe, but the model predicts it as Unsafe.
* _FN_ (False Negative): The total number of samples where the model predicts the negative class incorrectly, i.e., the actual class is Unsafe, but the model predicts it as Safe.  
The evaluation of the proposed CNN model reveals not only a significantly high level of accuracy but also almost equally high specificity and sensitivity (or recall) values. This outcome establishes the model as highly effective and well-suited for addressing the binary classification problem at hand.  
Lastly, we evaluated the impact of the SMOTE technique on the performance of the proposed CNN model by trying not to use SMOTE. Table 8 indicates remarkable decreases in accuracy and recall scores (by 5.9% and 11.0%, respectively) of the model when not using the SMOTE technique for balancing class distribution in the training set. This result  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Position**} & \multicolumn{6}{c}{**Feature Type**} \\ \cline{2-7}  & **orgi** & **prep** & **der1** & **der2** & **prep + der1** & **prep + der2** \\ \hline Skin, nape & 77.2 & 80.0 & 82.2 & 79.3 & 81.0 & 77.3 \\ Skin, back & 69.1 & 70.4 & 77.3 & 74.2 & 72.6 & 68.8 \\ Skin, tail & 61.2 & 73.4 & 64.1 & 66.9 & 66.3 & 58.5 \\ Skin, stomach & 81.7 & 82.5 & 81.5 & 83.7 & 83.9 * & 81.7 \\ Internal, nape & 80.0 & 78.9 & 78.9 & 73.8 & 80.0 & 75.5 \\ Internal, back & 74.2 & 74.9 & 66.5 & 70.4 & 69.7 & 74.2 \\ Internal, tail & 66.5 & 70.0 & 64.8 & 66.9 & 66.5 & 65.1 \\ Internal, stomach & 65.9 & 67.7 & 70.2 & 64.4 & 70.8 & 64.2 \\ All positions & 68.3 & 71.3 & 72.3 & 70.4 & 71.8 & 69.2 \\ \hline \hline \end{tabular}  
* The highest classification accuracy among CNN models.  
\end{table}
Table 7: Accuracy (%) on test sets of optimally tuned CNN models.  
confirms the essential use of SMOTE in dealing with a highly imbalanced dataset such as the one in our study.