# Multiclass Skin Cancer Classification Using Ensemble of Fine-Tuned Deep Learning Models
## 8 Results and Discussion  
The performance of the proposed models have been evaluated using the measures of accuracy, precision, recall, f1-score, and support. Tables 2 and 3 show the comparative classification performance of individual deep learners of ResNet, InceptionV3, DenseNet, InceptionResnetV2, VGG-19, and the proposed ensemble model. It is observed from the table that the ensemble model outperforms the individual models in terms of precision, recall, f1 score, and accuracy. The accuracy of individual learners of ResNet, InceptionV3, DenseNet, InceptionResnetV2, VGG-19 is 92%, 72%, 92%, 91%, and 91%, respectively. However, the accuracy measures of the majority voting, weighted averaging based ensemble, and weighted majority voting-based ensemble models are 98%, 98.2%, and 98.6%, respectively. Figure 5 shows that the accuracy of the ensemble approach is much higher than the individual models. Figure 5: Accuracy comparison of individual learners and their ensemble decision. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{5}{c}{**Classification Report of ResNet**} \\ \hline
**Skin Cancer Type** & **Precision** & **Recall** & **F1-Score** & **Support** \\ \hline AK & 0.85 & 0.95 & 0.89 & 261 \\ BCC & 0.91 & 0.94 & 0.93 & 292 \\ BKL & 0.95 & 0.92 & 0.93 & 306 \\ DF & 0.95 & 0.84 & 0.89 & 63 \\ MEL & 0.90 & 0.90 & 0.90 & 325 \\ NV & 0.93 & 0.94 & 0.93 & 305 \\ SCC & 0.96 & 0.82 & 0.88 & 173 \\ VASC & 0.93 & 0.97 & 0.95 & 72 \\ Accuracy & & & 0.92 & 1797 \\ Macro Avg. & 0.92 & 0.91 & 0.91 & 1797 \\ Weighted Avg. & 0.92 & 0.92 & 0.92 & 1797 \\ \hline \multicolumn{5}{c}{**Classification Report of DenseNet**} \\ \hline
**Skin Cancer Type** & **Precision** & **Recall** & **F1-Score** & **Support** \\ \hline AK & 0.95 & 0.88 & 0.91 & 261 \\ BCC & 0.89 & 0.94 & 0.91 & 292 \\ BKL & 0.92 & 0.95 & 0.93 & 306 \\ DF & 0.95 & 0.89 & 0.92 & 63 \\ MEL & 0.91 & 0.90 & 0.90 & 325 \\ NV & 0.92 & 0.95 & 0.94 & 305 \\ SCC & 0.89 & 0.86 & 0.87 & 173 \\ VASC & 0.99 & 0.96 & 0.97 & 172 \\ Accuracy & & & 0.92 & 1797 \\ Weighted Avg. & 0.93 & 0.91 & 0.92 & 1797 \\ \hline \multicolumn{5}{c}{**Classification Report of VGG-19**} \\ \hline
**Skin Cancer Type** & **Precision** & **Recall** & **F1-Score** & **Support** \\ \hline AK & 0.95 & 0.87 & 0.91 & 261 \\ BCC & 0.88 & 0.92 & 0.90 & 292 \\ BKL & 0.96 & 0.87 & 0.92 & 306 \\ DF & 0.90 & 0.90 & 0.90 & 63 \\ MEL & 0.87 & 0.91 & 0.89 & 325 \\ NV & 0.92 & 0.95 & 0.94 & 305 \\ SCC & 0.87 & 0.94 & 0.90 & 173 \\ VASC & 0.95 & 0.96 & 0.95 & 72 \\ Accuracy & & & 0.91 & 1797 \\ Macro Avg & 0.91 & 0.92 & 0.91 & 1797 \\ Weighted Avg. & 0.91 & 0.91 & 0.91 & 1797 \\ \hline \hline \multicolumn{5}{c}{**Classification Report of Inception V3**} \\ \hline
**Skin Cancer Type** & **Precision** & **Recall** & **F1-Score** & **Support** \\ \hline AK & 0.65 & 0.80 & 0.72 & 261 \\ BCC & 0.87 & 0.72 & 0.79 & 292 \\ BKL & 0.87 & 0.72 & 0.79 & 306 \\ DF & 0.69 & 0.43 & 0.53 & 63 \\ MEL & 0.73 & 0.72 & 0.73 & 325 \\ NV & 0.59 & 0.98 & 0.73 & 305 \\ SCC & 0.90 & 0.42 & 0.57 & 173 \\ VASC & 1 & 0.40 & 0.57 & 72 \\ Accuracy & & & 0.72 & 1797 \\ Macro Avg. & 0.79 & 0.65 & 0.68 & 1797 \\ Weighted Avg. & 0.77 & 0.72 & 0.72 & 1797 \\ \hline \hline \multicolumn{5}{c}{**Classification Report of Inception ResNet V2**} \\ \hline
**Skin Cancer Type** & **Precision** & **Recall** & **F1-Score** & **Support** \\ \hline AK & 0.98 & 0.93 & 0.91 & 261 \\ BCC & 0.92 & 0.93 & 0.93 & 292 \\ BKL & 0.91 & 0.93 & 0.92 & 306 \\ DF & 0.98 & 0.90 & 0.89 & 63 \\ MEL & 0.89 & 0.90 & 0.89 & 325 \\ NV & 0.96 & 0.93 & 0.95 & 305 \\ SCC & 0.87 & 0.84 & 0.86 & 173 \\ VASC & 0.99 & 0.92 & 0.95 & 72 \\ Accuracy & & & 0.91 & 1797 \\ Macro Avg. & 0.93 & 0.91 & 0.92 & 1797 \\ Weighted Avg. & 0.91 & 0.91 & 0.91 & 1797 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance comparison of individual learners with the ensemble approach.