## 3 Results
Melanoma (MEL) and melanocytic nevus (NV) exhibited slightly lower AUC values of 0.92 and 0.91, suggesting some challenges in differentiation. The micro-average AUC of 0.97 confirms robust overall performance across all classes, underscoring the model's ability to handle the complexities of multiclass skin lesion classification effectively. Figure 6: Confusion matrix of the classification model on the test set. Rows represent the actual lesion classes, while columns represent the predicted classes. The intensity of each cell in the heatmap corresponds to the number of predicted instances; lighter shades indicate higher values and thus stronger model confidence in those predictions. The strong diagonal dominance indicates high classification accuracy for most skin lesion types, with some misclassifications occurring between visually similar lesions. The entire workflow was repeated 30 times to account for the inherent randomness in processes such as balancing class samples, splitting the dataset into training and testing sets, and the stochastic nature of neural network training. This repetition ensures that the results are not biased by any random data partitioning or model initialization instance. By performing the process 30 times, the average of the performance metrics (e.g., accuracy, recall, precision) could be calculated, along with their error intervals at a 95% confidence level. This approach provides robust and statistically reliable metrics, minimizing the impact of random fluctuations and offering a more accurate assessment of the model's performance. The metrics presented in Table 2 indicate the model's classification performance. Recall values ranged from 55.85% \(\pm\) 2.33 for melanocytic nevs (NV) to 98.62% \(\pm\) 0.58 for acti \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Image** & **Actual Class** & **Predicted Class** & **Accurate Classified** \\ \hline  & NV & NV & True \\ \hline  & MEL & MEL & True \\ \hline  & VASC & MEL & False \\ \hline  & BCC & BCC & True \\ \hline  & NV & BKL & False \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Representative examples of correct and incorrect predictions made by the proposed classification methodology. Each row shows a skin lesion image, its actual class, the predicted class, and whether the classification was correct. Figure 7: Multiclass receiver operating characteristic (ROC) curves for each of the eight skin lesion classes. The micro-average ROC is also included to summarize overall model effectiveness. beratosis (AK), showing varying sensitivity in correctly identifying different lesion types. Similarly, false positive (FP) rates were lowest for AK (0.98% \(\pm\) 0.16) and vascular lesions (VASC) (0.97% \(\pm\) 0.10), reflecting high precision in these classes. Specificity remained consistently high across all classes, ranging from 94.75% \(\pm\) 0.44 for melanoma (MEL) to 99.03% \(\pm\) 0.10 for VASC, indicating the model's strong ability to identify true negatives. Precision followed a similar trend, with higher values for classes like AK (93.64% \(\pm\) 0.95) and VASC (93.37% \(\pm\) 0.60) and slightly lower for NV (63.92% \(\pm\) 1.43) and MEL (62.07% \(\pm\) 1.57). Overall accuracy averaged 95.33% \(\pm\) 0.45, with AK, SCC, and DF achieving the highest individual accuracies (above 98%). The F1 score, reflecting the harmonic mean of precision and recall, varied from 59.34% \(\pm\) 1.44 for NV to 96.05% \(\pm\) 0.65 for AK. The overall F1 score was 80.82% \(\pm\) 1.90, highlighting the model's balanced performance across all classes. The computational performance of the proposed method was evaluated on a standard laptop equipped with a 1.6 GHz Dual-Core Intel Core i5 processor and 8 GB of 1600 MHz DDR3 RAM.