# Comparison of Deep Transfer Learning Techniques in Human Skin Burns Discrimination
## 4 Results
### Results from Off-the-Shelf features and SVM  
Using off-the-shelf features and SVM classifier, a near perfect results were achieved using features in each of the three pre-trained models. The classification results were presented in Table 1 for ResNet50 features, Table 2 for ResNet101 features and Table 3 for ResNet152 features. The sensitivity and specificity of the classification output were calculated. Sensitivity (true positive rate) is the proportion of actual burn images that are correctly classified while specificity is the proportion of actual non burn images that are correctly classified [9]. Sensitivity is determined by the following mathematical equation--sensitivity \(=\frac{TP}{TP+FN}\times 100\); specificity is determined by the following mathematical equation; specificity \(=\frac{TN}{TN+FP}\times 100\). In each of the Tables 1-3 above, target (actual) classes are presented as columns while rows of the tables represent output (predicted) classes. The intersection where Burns in the second column and Burns in the second-row meet represent the true positives (TP); the intersection where PUB in the first column and PUB in the first-row meet represents true negatives (TN). FP stands for false positive which means PUB that were predicted by the algorithm as Burns which can be found at first cell in the second row (second cell in the second column). FN stands for false negative which means Burns that were predicted as by the algorithm as PUB which can be found at second cell of the first row (first cell of the second column). Table 4 presents the sensitivity and specificity of the SVM classifier for each of the features in the three models. Classification accuracy is the ratio of all correct predictions to the total number of all the samples used which can be calculated by the following mathematical equation--accuracy \(=\frac{TP+TN}{TP+TN+FP+FN}\times 100\). Table 5 shows the classification accuracies obtained by SVM classifier on all the feature sets. Near perfect results were obtained in each case, with best recognition accuracy using ResNet152 features in each of the approaches used. However, classification is more efficient using ResNet152 features which took approximately 1660 s to complete compared to 2116 and 2705 s for both ResNet101 and ResNet152 respectively. With off-the-shelf features from all the three feature sets, SVM performance is more impressive with ResNet152 and more efficient than the other two features from ResNet50 and ResNet152 as presented in Table 5. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Target Classes**} \\ \cline{2-4}
**Output Classes** & & PUB & Burns \\  & PUB & 1160 & 3 \\  & Burns & 0 & 1157 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Support Vector Machine (SVM) classification using ResNet50 features. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Target Classes**} \\ \cline{2-4}
**Output Classes** & & PUB & Burns \\  & PUB & 1160 & 3 \\  & Burns & 0 & 1157 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: SVM classification using ResNet101 features. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Target Classes**} \\ \cline{2-4}
**Output Classes** & & PUB & Burns \\  & PUB & 1160 & 1 \\  & Burns & 0 & 1159 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: SVM classification using ResNet152 features. Furthermore, the Receiver Operating Characteristics (ROC) curve is another important tool for analyzing the diagnostic performance of a binary classifier. ROC curve gives the trade-off between sensitivity and specificity as the threshold varies [29]. One important feature of ROC curve is the Area Under the Curve (AUC) which provides the overall summary of the classifier's performance. The AUC ranges from 0.5 to 1, where AUC of 0.5 or less indicates poor diagnosis (meaningless assessment) while AUC of 1 indicates perfect assessment. Figure 14 shows the ROC curve and the corresponding AUC values of the SVM classifier for each of the three sets of features.