## 3 Results and Discussion
### Performance Evaluation
The most common used evaluation measure is accuracy; Equation (1) shows how accuracy is computed, and the result is shown in Table 1. Accuracy evaluates the classifier effectiveness by giving the percentage of precisely classified samples. Error rate is a complement of accuracy which provides the percentage of incorrectly classified samples, which can be evaluated using Equation (2). \[accuracy\ =\ \frac{TN\ +\ TP}{FN\ +\ FP\ +\ TN\ +\ TP}. \tag{1}\]  
\[Error\ rate\ =\ \frac{FN+FP}{FN+FP+TN+TP}. \tag{2}\]  
Other performance evaluation measures include recall, precision, and _F1-score_. Recall, also known as sensitivity, measures the proportion of positive samples classified as positive out of the total number of positive samples, which can be computed using Equation (3). Precision gives the percentage of relevant samples predicted by the classifier out of the total predicted instances, which can be computed using Equation (4). In order to have balanced recall and precision values, a harmonic mean of these values is computed to return a single metric referred to as the _F1-score_, which can be computed using Equation (5). \[recall\ =\ \frac{TP}{TP+FN}. \tag{3}\]  
\[Precision\ =\ \frac{TP}{TP+FP}. \tag{4}\]  
\[F1-score\ =\ \frac{2}{recall^{-1}\ +precision^{-1}}. \tag{5}\]  
The results in Tables 14-16 show the computed precision, recall, and _F1-score_. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**True Class**} & \multicolumn{2}{c}{**Predicted Class**} \\ \cline{2-3}  & **Negative** & **Positive** \\ \hline Negative & 827 & 13 \\ Positive & 199 & 641 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Deep FC2 features and KNN classifier. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**True Class**} & \multicolumn{2}{c}{**Predicted Class**} \\ \cline{2-3}  & **Negative** & **Positive** \\ \hline Negative & 759 & 81 \\ Positive & 56 & 784 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Deep FC2 features and LR classifier. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**True Class**} & \multicolumn{3}{c}{**Predicted Class**} \\ \cline{2-3}  & **Negative** & **Positive** \\ \hline Negative & 829 & 11 \\ Positive & 56 & 784 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Deep FC2 features and KNN classifier. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Layer**} & **DT** & **SVM** & **NB** & **LR** & **KNN** \\ \hline FC1 & 90.39\% & 98.61\% & 91.10\% & 98.25\% & 98.92\% \\ FC2 & 89.34\% & 98.99\% & 90.64\% & 98.62\% & 98.01\% \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Computed precision. Moreover, other common performance evaluation measures for binary classification problems include the receiver operating characteristics (ROC) curve and precision-recall curve (PRC) [14; 26]. The ROC curve is used to provide a graphical visualisation of the performance of a classification algorithm. The graph depicts the trade-off between the true positive rate (TPR) and the false positive rate (FPR). The area under the ROC curve (AUC) is widely used to evaluate the performance of the classifier. The values of the AUC range between 0 and 1, whereby an AUC value between 0 and 0.5 indicates poor classification while an AUC value ranging between 0.5 and 1.0 indicates good classification, and AUC = 1 indicates perfect classification. Figure 8 depicts the ROC curve of different classifiers using 4096 FC1 features of the VGG16 model. Here, the figure shows the true positive rate on the \(y\)-axis (vertical) and the true negative rate on the \(x\)-axis (horizontal). It is used to visualise how the classifier effectively separates the two classes. AUC provides the degree of separability by estimating how much the classifier is capable of discriminating the two classes, where a higher AUC denotes better classification of burns as burns and healthy skin as healthy skin.