## 4 Experiment
### Comparison of the HAM10000 and ISIC 2019 Datasets
#### 4.4.1 Comparison with Non-Segmentation Methods
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{5}{c}{**Methods**} \\ \cline{2-5} \multicolumn{1}{c}{**Shifted MobileNetV2**} & **Shifted GoogLeNet** & **Shifted 2-Nets** & **The Proposed Method** \\ \hline Specificity & 95.20\% & 94.70\% & 95.30\% & 96.03\% \\ Sensitivity & 65.90\% & 58.10\% & 64.40\% & 70.71\% \\ Precision & 71.40\% & 68.50\% & 76.10\% & 75.15\% \\ F1 score & 67.00\% & 60.80\% & 67.80\% & 72.61\% \\ Accuracy & 81.90\% & 80.50\% & 83.20\% & 84.80\% \\ Parameters & 3.4 M & 7 M & 10.4 M & 1.8 M \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison with different methods on the HAM10000 dataset in the first experiment (macro-average). Figure 5: Confusion matrix of the four methods. In the third experiment, we follow the dataset divided according to the previous study [76]. The authors in reference [76] tested the skin classification on the ISIC-2019 dataset with different transfer learning models, such as state-of-the-art architecture EfficientNet. EfficientNet has eight models that start from B0 to B7 and achieve better efficiency and accuracy than earlier ConvNets. EfficientNet employs swish activation instead of applying the ReLU function (see reference [76]). Table 6 compares all approaches. All methods provide only the accuracy metric, except the proposed methods. EfficientNetB0 uses a small number of parameters. It is the simplest of all eight architectures in EfficientNet. However, the total parameters of EfficientNet-B0 are approximately three times that of our method (5 M compared with 1.8 M), whereas the accuracy is lower (81.75% compared with 82.56%). EfficientNet-B7 and ResNet-152 are the first and second rank in terms of accuracy, respectively. Both architectures have high parameters (66 and 50 M, respectively) and achieved a better result than our method (the proposed network uses less than 4% of the parameters compared to these two models). VGG19 is the worst method with the highest parameters (143 M) and the lowest accuracy (80.17%). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Accuracy** & **Specificity** & **Sensitivity** & **Parameters** \\ \hline HARTS [82] & 77.00\% & 83.00\% & 78.21\% & - \\ FTNN [83] & 79.00\% & 84.00\% & 79.54\% & - \\ VGG19 [85] & 81.00\% & 87.00\% & 82.46\% & 143 M \\ MobileNet V1 [68] & 82.00\% & 89.00\% & 84.04\% & 4.2 M \\ MobileNet V2 [86] & 84.00\% & 90.00\% & 86.41\% & 3.4 M \\ MobileNet V2-LSTM [29] & 85.34\% & 92.00\% & 88.24\% & \(\approx\)3.4 M \\ \hline The proposed method & 86.33\% & 97.72\% & 86.33\% & 1.8 M \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison with different methods on the HAM10000 dataset in the second experiment (micro-average). Figure 6: Bar chart results of various methods in the first experiment. We have already compared different methods in three experiments. Even with the highest efficiency result, our method has a weakness that could not control the imbalanced classes of two datasets: HAM10000 and ISIC2019. Data sampling methods, which are the future research work, can balance the classes distribution.