## 8 Comparison with Other Methodologies  
[PERSON] et al. (2019) developed a computer-aided diagnosis of melanoma detection. They used wavelet-decomposition and geometrical features of the lesion, and the classifiers SVM, random forest, logistic model tree, and naive Bayes. The dataset had 289 dermoscopy images: 114 malignant (M) and 175 benign (B) images, which were split by the training set with 40 M and 59 B images; validation set with 30 M and 57 B images; and, the test set with 44 M and 59 B images. They obtained an accuracy = 91.26%, and AUC = 0.937. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Class** & _Accuracy (\%)_ & _Precision (\%)_ & _Sensitivity (\%)_ & _Specificity (\%)_ \\ \hline AK & 95.61 \(\pm\) 0.26 & 38.81 \(\pm\) 2.15 & 47.56 \(\pm\) 2.79 & 97.33 \(\pm\) 0.13 \\ BCC & 89.48 \(\pm\) 0.28 & 57.72 \(\pm\) 1.29 & 69.07 \(\pm\) 0.63 & 92.51 \(\pm\) 0.28 \\ SCC & 97.19 \(\pm\) 0.13 & 34.48 \(\pm\) 4.10 & 13.84 \(\pm\) 1.19 & 99.32 \(\pm\) 0.11 \\ BKL & 90.30 \(\pm\) 0.21 & 55.55 \(\pm\) 3.59 & 28.97 \(\pm\) 1.90 & 97.34 \(\pm\) 0.29 \\ MEL & 86.85 \(\pm\) 0.18 & 81.83 \(\pm\) 2.11 & 33.84 \(\pm\) 0.59 & 98.37 \(\pm\) 0.19 \\ NEV & 80.06 \(\pm\) 0.55 & 73.36 \(\pm\) 0.65 & 95.58 \(\pm\) 0.39 & 63.95 \(\pm\) 0.86 \\ DER & 99.07 \(\pm\) 0.07 & 90.00 \(\pm\) 22.36 & 3.08 \(\pm\) 1.95 & 99.99 \(\pm\) 0.01 \\ VASC & 99.06 \(\pm\) 0.07 & 98.57 \(\pm\) 3.14 & 15.49 \(\pm\) 1.70 & 99.99 \(\pm\) 0.01 \\
**Mean \(\pm\) SD** & **92.20 \(\pm\) 6.74** & **66.30 \(\pm\) 23.46** & **38.43 \(\pm\) 31.12** & **93.60 \(\pm\) 12.22** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Mean \(\pm\) SD of five performance metrics for the test dataset of the Exp-3 with \(\alpha=0.4\). The test dataset has 7599 images. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Class** & _Accuracy (\%)_ & _Precision (\%)_ & _Sensitivity (\%)_ & _Specificity (\%)_ \\ \hline AK & 98.87 \(\pm\) 0.08 & 85.88 \(\pm\) 1.56 & 80.52 \(\pm\) 1.21 & 99.52 \(\pm\) 0.07 \\ BCC & 96.83 \(\pm\) 0.04 & 82.93 \(\pm\) 0.14 & 96.29 \(\pm\) 0.23 & 96.91 \(\pm\) 0.05 \\ SCC & 98.48 \(\pm\) 0.05 & 94.68 \(\pm\) 1.86 & 41.87 \(\pm\) 1.05 & 99.93 \(\pm\) 0.02 \\ BKL & 97.28 \(\pm\) 0.04 & 92.88 \(\pm\) 0.28 & 80.55 \(\pm\) 0.53 & 99.27 \(\pm\) 0.03 \\ MEL & 95.55 \(\pm\) 0.07 & 96.21 \(\pm\) 0.30 & 78.69 \(\pm\) 0.67 & 99.31 \(\pm\) 0.06 \\ NEV & 93.92 \(\pm\) 0.19 & 90.83 \(\pm\) 0.29 & 98.14 \(\pm\) 0.09 & 89.39 \(\pm\) 0.37 \\
**Mean \(\pm\) SD** & **96.82 \(\pm\) 1.85** & **90.57 \(\pm\) 5.19** & **79.35 \(\pm\) 20.24** & **97.39 \(\pm\) 4.06** \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Mean \(\pm\) SD of five performance metrics for the training dataset of the Exp-4 with \(\alpha=1.6\). The training dataset has 17,387 images. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Class** & _Accuracy (\%)_ & _Precision (\%)_ & _Sensitivity (\%)_ & _Specificity (\%)_ \\ \hline AK & 95.38 \(\pm\) 0.30 & 38.25 \(\pm\) 3.57 & 45.15 \(\pm\) 1.91 & 97.26 \(\pm\) 0.40 \\ BCC & 89.66 \(\pm\) 0.21 & 59.15 \(\pm\) 0.30 & 68.38 \(\pm\) 0.83 & 92.87 \(\pm\) 0.14 \\ SCC & 97.23 \(\pm\) 0.23 & 40.12 \(\pm\) 6.49 & 16.26 \(\pm\) 3.14 & 99.36 \(\pm\) 0.11 \\ BKL & 90.39 \(\pm\) 0.16 & 58.13 \(\pm\) 1.94 & 29.27 \(\pm\) 0.80 & 97.53 \(\pm\) 0.20 \\ MEL & 86.43 \(\pm\) 0.28 & 81.66 \(\pm\) 1.43 & 32.62 \(\pm\) 1.15 & 98.37 \(\pm\) 0.14 \\ NEV & 80.59 \(\pm\) 0.45 & 74.40 \(\pm\) 0.40 & 95.68 \(\pm\) 0.28 & 64.17 \(\pm\) 0.83 \\
**Mean \(\pm\) SD** & **89.95 \(\pm\) 6.05** & **58.62 \(\pm\) 17.54** & **47.89 \(\pm\) 29.30** & **91.60 \(\pm\) 13.62** \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Mean \(\pm\) SD of five performance metrics for the test dataset of the Exp-4 with \(\alpha=1.6\). The test dataset has 7452 images. [PERSON] et al. [34] built two computer-aided diagnoses of dermoscopy images for melanoma detection: one uses global features and the other local features. They employed the classifiers KNN, AdaBoost, SVM-RBF, bag of features (BoF). The dataset had 176 dermoscopy images: 25 MEL and 151 NEV images.