## 4 Experimental Results
### Performance Evaluation Analysis
Moreover, the PREC metric shows performance values between 0.86 \(\leq\) PREC \(\leq\) 1 on the ISIC2019 dataset while the optimized model showed better performance for the HAM10000 and ISIC2018 datasets. For the PREC metric, best performance values are close to 1, where 0 exhibits a perfect negative performance and 1 exhibits a perfect positive performance. Hence, using this measure, the performance values of the optimized CNN model, can indicate how good the results are over all the datasets. In addition, the performance of the optimized CNN model is evaluated using the relevant metrics and by examining the training and testing accuracies and losses. These are essential indicators that provide insight into the performance of the proposed model and where any inconsistencies lie. Plotting the loss and accuracy functions during training provides valuable insights into the model's performance. A consistent decrease in the loss function indicates improvement, while fluctuation or increase could mean overfitting, optimization issues, or other data or model architecture inconsistencies. Thus, tracking the loss function can help identify and monitor these inconsistencies. The accuracy and loss curves for the HAM10000, ISIC2018, and ISIC2019 datasets are shown in Figures 5-7. These figures demonstrate the learning ability of the optimized CNN through experiments, with increasing accuracy observed in the training and testing processes. The robustness of the model is evidenced by the accuracy values reaching over 99% with low numbers of training epochs for datasets with a reasonable number of samples. As seen from the figures, some fluctuations in accuracy values and losses occur due to prediction errors during some epochs. The figures for the ISIC2018 and ISIC2019 datasets display the training and testing curves and losses. The results of this research are crucial for developing efficient computer-aided diagnosis systems for skin cancer classification, enabling doctors to make informed decisions based on metadata information. The drive to create accurate and trustworthy computer-aided diagnosis systems for early skin cancer treatment. A confusion matrix technique is another method to summarize an algorithm's classification performance. Calculation of the confusion matrix provides a way of finding the number of classifications that are confused by the algorithm. Correct and incorrect predictions are broken down into classes. Our results using a confusion matrix are summarized in Figure 8, for each dataset. The diagonal view shows the correct prediction for each class and the off-diagonal blocks show confusion between the corresponding classes in that row and column. Figure 9 shows the overall numbers of incorrect prediction values for each class. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Class** & **PREC** & **SN** & **F-Score** & **ACC** & **SP** & **DICE** \\ \hline NEV & 0.94 & 0.86 & 0.90 & 0.92 & 0.91 & 0.89 \\ MEL & 0.89 & 0.94 & 0.91 & 0.91 & 0.92 & 0.91 \\ KER & 0.97 & 0.99 & 0.98 & 0.95 & 0.96 & 0.94 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The optimized CNN model evaluated on the ISIC2017 dataset. Figure 5: The loss and the accuracy training and validation curves for the HAM10000 dataset. Figure 6: The loss and the accuracy training and validation curves for the ISIC2018 dataset. Figure 7: The loss and the accuracy training and validation curves for the ISIC2019 dataset. Figure 8: The Confusion Matrices for the HAM10000, ISIC2018 and ISIC2019 datasets, respectively. Figure 9: _Cont._