## 3 Results  
We randomly selected images for classification. The classes in the dataset were balanced, with 1840 images used for each type of skin lesion to prevent classification bias. Then, the deep learning model described in the \"Classification\" section was trained using a data split of 30% for testing and 70% for training. To assess the classification performance of the proposed methodology, a variety of standard metrics were employed. Recall (Equation (17)) quantifies the proportion of actual positives correctly identified by the model, emphasizing its ability to capture all instances of a given class. False Positive Rate (Equation (18)) reflects the proportion of negative instances misclassified as positive, serving as a complement to specificity. Specificity (Equation (19)) measures the model's capability to correctly identify negative instances, calculated as one minus the FP rate. Precision (Equation (20)) indicates the percentage of correctly predicted positive instances, demonstrating the reliability of positive predictions. Accuracy (Equation (21)) provides an overall evaluation of the model, representing the ratio of correctly classified instances (both positive and negative) to the total number of instances. Lastly, the F1 score (Equation (22)) offers a balanced measure of precision and recall, expressed as their harmonic mean, making it particularly useful in scenarios with imbalanced datasets. Collectively, these metrics provide a comprehensive view of the model's classification performance. \[Recall(C_{i})=\frac{TP_{i}}{TP_{i}+FN_{i}}, \tag{17}\]  
\[FP\ rate(C_{i})=\frac{FP_{i}}{FP_{i}+TN_{i}}, \tag{18}\]  
\[Specificity(C_{i})=1-FP\ rate(C_{i})\, \tag{19}\]  
\[Precision(C_{i})=\frac{TP_{i}}{TP_{i}+FP_{i}}, \tag{20}\]  
\[accuracy(C_{i})=\frac{TP_{i}+TN_{i}}{TP_{i}+TN_{i}+FP_{i}+FN_{i}}, \tag{21}\]  
\[F1\ score(C_{i})=\frac{2\cdot Precision(C_{i})\cdot Recall(C_{i})}{Precision(C_{i})+ Recall(C_{i})}, \tag{22}\]where  
\(TP_{i}\): true positives for class \(i\). \(TN_{i}\): true negatives for class \(i\). \(FP_{i}\): false positives for class \(i\). \(FN_{i}\): false negatives for class \(i\). The confusion matrix, Figure 6, reveals that the classification model performs well overall, with diagonal solid dominance indicating accurate predictions for most classes. High accuracy is observed for vascular lesions (VASC), dermatofibroma (DF), squamous cell carcinoma (SCC), and actinic keratosis (AK), with minimal misclassifications. However, notable confusion exists between melanocytic nevus (NV) and melanoma (MEL), as well as between MEL and basal cell carcinoma (BCC), suggesting overlapping features among these lesion types. Additionally, benign keratosis (BKL) is occasionally misclassified as NV. Table 1 presents a selection of correctly and incorrectly classified skin lesion images to provide qualitative insights into the model's performance. As shown, the model successfully identifies certain lesion types such as nevus (NV), melanoma (MEL), and basal cell carcinoma (BCC), even in visually complex cases. However, it also exhibits misclassifications, notably predicting melanoma in place of vascular lesions (VASC) and benign keratosis (BKL) instead of nevus (NV). The ROC curves in Figure 7 illustrate the performance of the multiclass classification model across eight skin lesion classes, showing the relationship between the actual positive rate (sensitivity) and the false positive rate. The area under the curve (AUC) values indicates high classification performance for all classes. Actinic keratosis (AK) and dermatofibroma (DF) achieved perfect classification with an AUC of 1.00, indicating the model's ability to distinguish these classes with no errors. Similarly, vascular lesions (VASC) and squamous cell carcinoma (SCC) demonstrated near-perfect separability with AUC values of 0.99. Benign keratosis (BKL) and basal cell carcinoma (BCC) followed by AUC values of 0.94 and 0.95, respectively, reflecting strong performance despite minor overlaps.