## 7 Results
The mean of five realizations was reported in Table 6. The Exp-3 for the training dataset gave the performance metric of \(accuracy=97.31\pm 2.14\%\), \(precision=91.90\pm 6.28\%\), \(sensitivity=64.39\pm 32.93\%\), and \(specificity=97.80\pm 4.15\%\). For the test dataset the computer-aided diagnosis got \(accuracy=92.20\pm 6.74\%\), \(precision=66.30\pm 23.46\%\), \(sensitivity=38.43\pm 31.12\%\), and \(specificity=93.60\pm 12.22\%\), Table 7. Finally, experiment 4 (Exp-4) did not consider the DER and VASC classes, and the dataset of 24,839 images was randomly divided into 70% for the training dataset (17387 images) and 30% for the test dataset (7452 images). The best performance was obtained with \(\alpha~{}=~{}1.6\). Five realizations were executed for the training dataset obtaining an \(accuracy=96.82\pm 1.85\%\), \(precision=90.57\pm 5.19\%\), \(sensitivity=79.35\pm 20.24\%\), and \(specificity=97.39\pm 4.06\%\), Table 8. For the test dataset it had \(accuracy=89.95\pm 6.05\%\), \(precision=58.62\pm 17.54\%\), \(sensitivity=47.89\pm 29.30\%\), and \(specificity=91.60\pm 13.62\%\), Table 9. As observed, in the performance of the training data metrics in Exp-4, when the classes with fewer images were eliminated, the \(accuracy\), \(precision\), and \(specificity\) remained in the same range as Exp-3 but increases the \(sensitivity\). Remember that the training methodologies in Exp-3 and Exp-4 did not see the images in the test database, the accuracy and specificity in both experiments showed excellent performance. However, the precision and sensitivity will need to be improved in the next stage of this work. There are several factors to consider that could affect the reliability of computer-aided diagnosis methodologies. The first thing to be analyzed is the stage of the extraction of characteristics, to determine if it is required to add more elements to the vector or if there are redundant elements. Afterwards, we must always keep in mind that we are working with unbalanced classes, which implies selecting how to handle them, using subsampling, oversampling, a hybrid between them or the assembly techniques, and the type of classifiers that are being used for the assembly. \begin{table}
\begin{tabular}{l r r r r r r r r} \hline \hline  & & & & & \multicolumn{6}{c}{**Training Dataset**} \\ \cline{2-9}  & **AK** & **BCC** & **SCC** & **BKL** & **MEL** & **NEV** & **DER** & **VASC** \\  & **867** & **3323** & **628** & **2624** & **4522** & **12,875** & **239** & **253** \\ \hline AK & 662 & 91 & 3 & 16 & 5 & 90 & 0 & 0 \\ BCC & 22 & 3203 & 0 & 13 & 14 & 70 & 1 & 0 \\ SCC & 34 & 224 & 276 & 11 & 2 & 81 & 0 & 0 \\ BKL & 27 & 144 & 4 & 2105 & 39 & 305 & 0 & 0 \\ MEL & 37 & 111 & 5 & 85 & 3597 & 687 & 0 & 0 \\ NEV & 14 & 96 & 1 & 40 & 65 & 12,659 & 0 & 0 \\ DER & 3 & 54 & 3 & 10 & 14 & 108 & 47 & 0 \\ VASC & 3 & 33 & 3 & 5 & 10 & 105 & 0 & 94 \\
**Exp-1 response** & **802** & **3956** & **295** & **2285** & **3746** & **14,105** & **48** & **94** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Confusion matrix of the Exp-1 with \(\alpha=-2.0\). The dataset has 25,331 images. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Class** & _Accuracy (\%)_ & _Precision (\%)_ & _Sensitivity (\%)_ & _Specificity (\%)_ \\ \hline AK & 98.62 & 82.68 & 76.47 & 99.42 \\ BCC & 96.78 & 82.63 & 96.21 & 96.88 \\ SCC & 98.55 & 94.98 & 45.22 & 99.94 \\ BKL & 97.22 & 92.78 & 79.88 & 99.27 \\ MEL & 95.73 & 96.53 & 79.39 & 99.27 \\ NEV & 94.16 & 91.12 & 98.31 & 89.69 \\
**Mean \(\pm\) SD** & \(\mathbf{96.85\pm 1.71}\) & \(\mathbf{90.12\pm 6.07}\) & \(\mathbf{79.25\pm 19.06}\) & \(\mathbf{97.42\pm 3.93}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Mean \(\pm\) SD of five performance metrics for the training dataset of the Exp-3 with \(\alpha=0.4\). The training dataset has 17,732.