## 4 Experimental Results
### Performance Evaluation Analysis  
This section presents the optimized CNN model's experimental results of skin lesion classification. The model performance was assessed through the six evaluation metrics and tested on 20% of the augmented HAM10000, ISIC2018, and ISIC2019 datasets. The evaluation metrics for the predicted classes using the testing datasets are shown in Tables 3-6. These results display impressive results for all evaluation metrics of each skin lesion type and indicate that the optimized CNN model effectively identified and differentiated different skin lesion types from the augmented datasets. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Class** & **PREC** & **SN** & **F-Score** & **ACC** & **SP** & **DICE** \\ \hline NEV & 0.98 & 1.00 & 0.99 & 0.96 & 0.96 & 0.96 \\ MEL & 0.98 & 0.99 & 0.98 & 0.96 & 0.96 & 0.96 \\ BCC & 0.92 & 0.97 & 0.95 & 0.96 & 0.96 & 0.96 \\ BKL & 0.99 & 0.97 & 0.98 & 0.96 & 0.96 & 0.96 \\ AKIEC & 0.95 & 0.91 & 0.93 & 0.96 & 0.96 & 0.96 \\ VASC & 0.90 & 0.88 & 0.89 & 0.96 & 0.96 & 0.96 \\ DF & 1.00 & 0.99 & 1.00 & 0.96 & 0.96 & 0.96 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The optimized CNN model evaluated on the ISIC2018 dataset. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Hyperparameter** & **Interval Value** & **Hyperparameter** & **Interval Value** \\ \hline Momentum & [0.1, 0.99] & Batch Size & [8, 16, 32,..., 256] \\ \hline Epochs & [25, 50, 75,..., 500] & Learning Rate & [0.01, 0.1] \\ \hline Dropout & [0.1, 0.9] & \# of Nodes & [8,16,32,..., 256] \\ \hline Activation Function & (Relu, Sigmoid, Linear) & \# of filters & [8,16,..., 256] \\ \hline Depth of the network & [1–15] & \# of dense layers & [1–6] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Hyperparameters range of CNN \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Class** & **PREC** & **SN** & **F-Score** & **ACC** & **SP** & **DICE** \\ \hline NEV & 1.00 & 1.00 & 1.00 & 0.99 & 0.99 & 0.99 \\ MEL & 0.99 & 1.00 & 1.00 & 0.99 & 0.99 & 0.99 \\ BCC & 0.96 & 1.00 & 0.98 & 0.99 & 0.99 & 0.99 \\ BKL & 1.00 & 1.00 & 1.00 & 0.99 & 0.99 & 0.99 \\ AKIEC & 0.97 & 0.99 & 0.98 & 0.99 & 0.99 & 0.99 \\ VASC & 1.00 & 0.91 & 0.95 & 0.99 & 0.99 & 0.99 \\ DF & 1.00 & 1.00 & 1.00 & 0.99 & 0.99 & 0.99 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The optimized CNN model evaluated on the HAM10000 dataset. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Class** & **PREC** & **SN** & **F-Score** & **ACC** & **SP** & **DICE** \\ \hline NEV & 0.98 & 1.00 & 0.99 & 0.96 & 0.96 & 0.96 \\ MEL & 0.98 & 0.99 & 0.98 & 0.96 & 0.96 & 0.96 \\ BCC & 0.92 & 0.97 & 0.95 & 0.96 & 0.96 & 0.96 \\ BKL & 0.99 & 0.97 & 0.98 & 0.96 & 0.96 & 0.96 \\ AKIEC & 0.95 & 0.91 & 0.93 & 0.96 & 0.96 & 0.96 \\ VASC & 0.90 & 0.88 & 0.89 & 0.96 & 0.96 & 0.96 \\ DF & 1.00 & 0.99 & 1.00 & 0.96 & 0.96 & 0.96 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The optimized CNN model evaluated on the ISIC2019 dataset. The results show that the optimized CNN model performed exceptionally well when tested on the HAM10000 and ISIC2018 datasets. The accuracy was 99% when tested on the HAM10000 dataset and 94% on the ISIC2019 dataset. As shown in Table 3, all predicted classes in the augmented HAM10000 dataset performed well, indicating that the optimized CNN model can accurately differentiate between various features for each class. The F-score values, which ranged from 0.95 to 1, further demonstrate the effectiveness of the optimized CNN model in each class. Furthermore, Table 4 shows that all classes from the augmented ISIC2018 dataset produced high evaluation results. The results in Table 5 show an F-score ranging between 0.84 \(\leq\) F-score \(\leq\) 1. From the defined ranges of classified F-score values, an F-score value greater than 80% is acceptable, but values less than 50% are not. Hence, the F-score range for this application is considered acceptable, since its F-score values fall above 80%.