# 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
\begin{table}
\begin{tabular}{c c c c c} \hline
**Classification Algorithms** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline EfficientNetB0-PCA DT & 90.40 \(\pm\) 2.62 & 90.55 \(\pm\) 2.59 & 90.46 \(\pm\) 2.60 & 90.41 \(\pm\) 2.61 \\ EfficientNetB0-PCA RF & 90.92 \(\pm\) 2.23 & 91.04 \(\pm\) 2.22 & 91.04 \(\pm\) 2.20 & 90.85 \(\pm\) 2.21 \\ EfficientNetB0-PCA NN & 90.93 \(\pm\) 2.16 & 91.17 \(\pm\) 2.34 & 90.96 \(\pm\) 2.17 & 90.93 \(\pm\) 2.16 \\ EfficientNetB0-PCASVM & 91.00 \(\pm\) 2.11 & 91.06 \(\pm\) 2.14 & 91.04 \(\pm\) 2.12 & 91.04 \(\pm\) 2.14 \\ \hline \end{tabular}
\end{table} 
Table 1: Performance metrics of classification algorithms for ISIC dataset. the same trend, with values of 91.676%, 91.348%, and 91.676%, respectively, indicating more variability in performance. EfficientNetB0-PCA SVM stood out with the highest mean accuracy of 93.00% and the lowest standard deviation of 1.70%, reflecting excellent and consistent performance. Its precision, recall, and F1-score were also the highest among all algorithms, with values of 93.185%, 92.975%, and 93.185%, respectively, and the lowest standard deviations, highlighting the robustness and reliability of this classifier. In summary, EfficientNetB0-PCA SVM surpasses other classifiers in terms of accuracy, precision, recall, and F1-score, with the additional advantage of lower performance variability, making it an exceptionally reliable choice for classification tasks in our model. Based on our results, the performance metrics provide an overview of how each algorithm classifier performed in our model, as depicted in Figure 4. The EfficientNetB0-PCA SVM classifier had the best overall performance, followed by EfficientNetB0-PCA RF and EfficientNetB0-PCA NN, while the EfficientNetB0-PCA DT classifier performed somewhat worse. The experimental findings clearly show that the EfficientNetB0-PCA SVM classifier beat the other methods in terms of accuracy, precision, recall, and the F1-score. As a result, we elected to use EfficientNetB0-PCA SVM algorithms in our model to identify and classify skin cancers. The excellent accuracy, as well as the balanced precision and recall values, demonstrate EfficientNetB0-PCA SVM's ability to effectively detect cases of skin cancer. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classification** & \multirow{2}{*}{**Accuracy**} & \multirow{2}{*}{**Precision**} & \multirow{2}{*}{**Recall**} & \multirow{2}{*}{**F1-Score**} \\
**Algorithms** & & & & \\ \hline EfficientNetB0-PCA DT & 91.38 \(\pm\) 2.76 & 91.676 \(\pm\) 2.820 & 91.348 \(\pm\) 2.903 & 91.676 \(\pm\) 2.820 \\ EfficientNetB0-PCA RF & 91.88 \(\pm\) 2.03 & 92.106 \(\pm\) 2.046 & 91.855 \(\pm\) 2.144 & 92.106 \(\pm\) 2.046 \\ EfficientNetB0-PCA NN & 92.60 \(\pm\) 1.89 & 92.845 \(\pm\) 2.023 & 92.579 \(\pm\) 1.974 & 92.845 \(\pm\) 2.023 \\ EfficientNetB0-PCA NN & 93.00 \(\pm\) 1.70 & 93.185 \(\pm\) 1.825 & 92.975 \(\pm\) 1.797 & 93.185 \(\pm\) 1.825 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance metrics of classification algorithms for DermIS dataset. Figure 3: Visualization of the performance metrics for algorithms classifiers. The comparison study provided us with useful insights into the performance of several algorithms, which influenced our decision to choose the EfficientNetB0-PCA SVM classifier for skin cancer diagnosis and categorization. The experimental results showed how each model and algorithm combination performed in terms of accuracy, precision, recall, F1 score, and support. Our goal in comparing these results was to discover the most powerful algorithm that delivers high accuracy in skin cancer categorization.