# An Efficient AdaBoost Algorithm for Enhancing Skin Cancer Detection and Classification
## 4 Experimental Results
### Evaluation Metrics of the Algorithms and Classifiers That Were Applied in the Proposed Model
#### 4.2.1 Performance Evaluation of Classification Algorithms  
Table 1 displays the mean and standard deviation of accuracy, precision, recall, and F1-score for each classification algorithm on the ISIC dataset and DermIS. Table 1 presents the performance metrics for four classification algorithms evaluated for skin cancer classification using the ISIC dataset. The models compared include EfficientNetB0 with PCA followed by Decision Tree (DT), Random Forest (RF), Neural Network (NN), and SVM. The EfficientNetB0-PCA DT model achieved an accuracy of 90.40% with a standard deviation of 2.62%, while the EfficientNetB0-PCA RF model showed a slightly better performance with an accuracy of 90.92% and a standard deviation of 2.23%. The EfficientNetB0-PCA NN model achieved a mean accuracy of 90.93% with a standard deviation of 2.16%, demonstrating high precision and stability. The EfficientNetB0-PCA SVM model achieved the highest accuracy of 91.00% with the lowest standard deviation of 2.11%, indicating the best overall performance and stability among the models compared. The metrics are reported as \"mean \(\pm\) standard deviation\" to provide insight into the average performance and variability across different cross-validation folds, ensuring a robust comparison and highlighting the reliability of the model in classifying skin cancer. This approach emphasizes the effectiveness of using EfficientNetB0 for feature extraction combined with PCA for dimensionality reduction and demonstrates that the SVM classifier outperforms other classifiers in this setup. These results clearly show that EfficientNetB0-PCA SVM outperforms the other classification algorithms in terms of accuracy, precision, recall, and F1-score for skin cancer classification using the ISIC dataset. As shown in Figure 3, The graphical plot highlights the comparative performance of each model, where EfficientNetB0-PCA SVM consistently achieves the highest metrics across all evaluation criteria. The bar representing the SVM model is noticeably higher, indicating superior accuracy (91.00 \(\pm\) 2.11%) and demonstrating the least variability, which underscores its reliability and robustness in classification tasks. In contrast, the Decision Tree model, despite its simplicity, shows the lowest performance with an accuracy of 90.40 \(\pm\) 2.62%, indicating higher variability and less stability. The Random Forest and Neural Network models perform better than the Decision Tree but still fall short compared to the SVM model. The precision, recall, and F1-score metrics follow a similar trend, reinforcing the conclusion that the SVM model provides a more balanced and consistent performance. This visualization effectively communicates the advantage of using EfficientNetB0-PCA SVM, making it a promising candidate for accurate and reliable skin cancer classification. Table 2 provides a comparison of the performance of various classification algorithms in our model by showing their mean accuracy, standard deviation, precision, recall, and F1-score, all expressed as percentages. The EfficientNetB0-PCA NN classifier achieved a mean accuracy of 92.60% with a standard deviation of 1.89%, indicating strong and consistent performance. Its precision, recall, and F1-score were also high, with mean values of 92.845%, 92.579%, and 92.845%, respectively, and low standard deviations. The EfficientNetB0-PCA RF classifier had a slightly lower mean accuracy of 91.88% and a standard deviation of 2.03%. Its precision and F1-score were 92.106%, and recall was 91.855%, showing reliable but slightly less consistent performance compared to the NN model. The EfficientNetB0-PCA DT classifier had the lowest performance, with a mean accuracy of 91.38% and a higher standard deviation of 2.76%. Its precision, recall, and F1-score followed