## 4 Evaluation of Model Performance
### Experimental Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Net** & **Input Size** & **Accuracy** & **Avg. AUC** & **REF** \\ \hline \multirow{3}{*}{DenseNet-201} & 512 \(\times\) 512 & 86.2\% & 98.0\% & \\ \cline{2-5}  & 256 \(\times\) 256 & 82.0\% & 97.5\% & \\ \cline{2-5}  & 128 \(\times\) 128 & 75.2\% & 95.0\% & \\ \hline \multirow{3}{*}{DenseNet-121} & 512 \(\times\) 512 & 83.4\% & 97.2\% & \\ \cline{2-5}  & 256 \(\times\) 256 & 80.9\% & 96.6\% & \\ \cline{2-5}  & 128 \(\times\) 128 & 73.9\% & 94.2\% & \\ \hline \multirow{3}{*}{SEResNeXt-101} & 512 \(\times\) 512 & 85.7\% & 98.1\% & \\ \cline{2-5}  & 256 \(\times\) 256 & 83.1\% & 97.8\% & \\ \cline{2-5}  & 128 \(\times\) 128 & 74.5\% & 96.0\% & \\ \hline \multirow{3}{*}{SEResNeXt-50} & 512 \(\times\) 512 & 86.7\% & 98.2\% & \\ \cline{2-5}  & 256 \(\times\) 256 & 81.8\% & 97.5\% & \\ \cline{2-5}  & 128 \(\times\) 128 & 76.1\% & 95.8\% & \\ \hline \multirow{3}{*}{ResNet-18} & 512 \(\times\) 512 & 80.6\% & 97.5\% & \\ \cline{2-5}  & 256 \(\times\) 256 & 78.9\% & 96.8\% & \\ \cline{2-5}  & 128 \(\times\) 128 & 70.8\% & 93.7\% & \\ \hline \multirow{3}{*}{ResNet-50} & 512 \(\times\) 512 & 84.1\% & 97.7\% & \\ \cline{2-5}  & 256 \(\times\) 256 & 79.6\% & 96.5\% & \\ \cline{2-5}  & 128 \(\times\) 128 & 69.2\% & 93.0\% & \\ \hline \multirow{3}{*}{ResNet-152} & 512 \(\times\) 512 & 86.1\% & 97.8\% & \\ \cline{2-5}  & 256 \(\times\) 256 & 80.2\% & 97.2\% & \\ \cline{2-5}  & 128 \(\times\) 128 & 72.3\% & 94.6\% & \\ \hline \multirow{2}{*}{_Our Method_} & **50 \(\times\) 256** & **88.6**\% & **94.9**\% & - \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance comparison result with a well-known CNN models for the ISIC 2018 dataset. Table 7 shows the results of performance validation using the ISIC 2017 dataset. For the existing model [37], a local binary pattern was applied to the original image to obtain the results. As shown in Table 7, the proposed method yielded a high accuracy of 89.3% in melanoma recognition, as well as demonstrating a high performance in terms of the area under the curve (AUC) (87.1%) and sensitivity (SENS) (89.0%). In terms of seborrheic keratosis recognition, the accuracy of the proposed model was low (i.e., 86.3%); similarly, its AUC (91.6%) and specificity (86.3%) were low, although its SENS was higher (86.0%) than that of the other models. Table 8 summarizes the comparison between the results yielded by the proposed method and those reported in previous studies [31; 37; 38; 39]. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**REF** & **Team/Authors** & **Accuracy** & \begin{tabular}{c} **Sensitivity** \\ **for Melanoma** \\ \end{tabular} & \
\begin{tabular}{c} **Avg.** \\ **AUC** \\ \end{tabular} &
\begin{tabular}{c} **Avg.** \\ **Specificity** \\ \end{tabular} \\ \hline
[33] & [PERSON] et al. & 88.5\% & 76.0\% & 98.3\% & 83.3\% \\ \hline
[34] & [PERSON] et al. & 85.6\% & 80.1\% & 98.7\% & 98.4\% \\ \hline
[35] & [PERSON] et al. & 86.2\% & - & 98.1\% & - \\ \hline
[36] & [PERSON] et al. & 85.3\% & 78.9\% & 97.5\% & 97.3\% \\ \hline  & [PERSON] et al. & 84.5\% & 70.2\% & 97.8\% & 98.0\% \\ \cline{2-6}  & [PERSON] & 89.5\% & 77.8\% & 98.2\% & 98.1\% \\ \cline{2-6}  & [PERSON] et al. & 87.4\% & 58.5\% & 97.9\% & 99.2\% \\ \cline{2-6}  & MRCN & 87.2\% & 86.0\% & 95.3\% & 97.9\% \\ \cline{2-6}
[11] & IPM\_HPC & 86.6\% & 83.0\% & 97.6\% & 97.6\% \\ \cline{2-6}  & [PERSON] & 83.6\% & 71.9\% & 97.5\% & 98.2\% \\ \cline{2-6}  & [PERSON], [PERSON], et al. (CMEL = 1.0) & 86.4\% & 77.2\% & 97.4\% & 97.1\% \\ \cline{2-6}  & [PERSON], [PERSON], et al. (CMEL = 1.8) & 85.2\% & 86.0\% & 97.3\% & 96.3\% \\ \hline - & _Our Method_ & **88.6\%** & **71.7\%** & **94.9\%** & **97.1\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance comparison result with a previous related references for the ISIC 2018 dataset.