## 4 Experiment
### Comparison of the HAM10000 and ISIC 2019 Datasets
#### 4.4.1 Comparison with Non-Segmentation Methods  
Table 3 presents the classification results of all approaches in three experiments. In the first experiment, the HAM10000 database is divided into two parts: one part with 8008 training images and the other part with 2007 testing images. Four methods in Table 3 provide the confusion matrix of the HAM10000 classification, including Shifted MobileNetV2, Shifted GoogLeNet, Shifted 2-Nets, and the proposed method. Shifted 2-Nets introduces an aggregate of enhanced CNN joined (ensemble of the DL approach) with a test-time regularly spaced shifting method for skin recognition. Shifted 2-Nets reached 83.20% accuracy (10.4 M parameters), whereas our framework produced 84.80% accuracy (1.8 M parameters). The proposed framework is more efficient than Shifted GoogLeNet, MobileNetV2, and Shifted 2-Nets. Figure 5 shows the confusion matrix of the four methods. The first note is that the proposed method has a lower wrong classification than other methods. Additionally, the number of right predictions has improved for most categories: AKIEC, BCC, DF, and MEL categories. BKL, NV, and VASC are three experiments that our framework ranks as the third, second, and third positions among four approaches, respectively. We calculate the following metrics from the obtained confusion matrix: specificity, sensitivity, precision, and F1 score. Table 4, which is visualized in Figure 6, presents our proposed framework's performance and various approaches with five metrics: accuracy, specificity, sensitivity, precision, and F1 score (macro-average). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Experiment** & **Method** & **ACC** \\ \hline \multirow{8}{*}{Experiment 1} & PNASNet [77] & 76.00\% \\  & ResNet-50 + gcForest [78] & 80.04\% \\  & VGG-16 + GoogLeNet & \\  & Ensemble [79] & 81.50\% \\  & Densenet-121 with SVM [80] & 82.70\% \\  & DenseNet-169 [80] & 81.35\% \\  & Bayesian DenseNet-169 [81] & 83.59\% \\  & Shifted MobileNetV2 [23] & 81.90\% \\  & Shifted GoogleNet [23] & 80.50\% \\  & Shifted 2-Nets [23] & 83.20\% \\ \cline{2-3}  & The proposed method & 84.80\% \\ \hline \multirow{8}{*}{Experiment 2} & HARTS [82] & 77.00\% \\  & FTNN [83] & 79.00\% \\  & CNN [84] & 80.00\% \\  & VGG19 [85] & 81.00\% \\  & MobileNet V1 [68] & 82.00\% \\  & MobileNet V2 [86] & 84.00\% \\  & MobileNet V2-LSTM [29] & 85.34\% \\ \cline{2-3}  & The proposed method & 86.33\% \\ \hline \multirow{8}{*}{Experiment 3} & VGG19 [85] & 80.17\% \\  & ResNet-152 [64] & 84.15\% \\ \cline{1-1}  & Efficient-B0 [87] & 81.75\% \\ \cline{1-1}  & Efficient-B7 [87] & 84.87\% \\ \cline{1-1} \cline{2-3}  & The proposed method & 82.56\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison with different methods on the HAM10000 and ISIC2019 datasets. In the second experiment, the HAM10000 database is split into two parts: 90% of the total images are the training part, and the rest are the testing part. None of the methods in Table 3 in the second experiment yield the precision, F1 score, and confusion matrix (micro-average metric). We use three metrics to evaluate the performance of various methods: accuracy, sensitivity, and specificity. Table 5 presents the outcomes of all approaches. Figure 7 visualizes the results of Table 5. Our framework achieves the highest performance in terms of accuracy and specificity. Meanwhile, the MobileNet V2 with long short-term memory (LSTM) [29] component has the highest sensitivity. MobileNet V2 improves efficiency more than the first version MobileNet V1. Consequently, the number of parameters decreases 19% from 4.2 to 3.4 M. MobileNet V2 is approximately two times the parameters of our framework (1.8 M), even with lower parameters. Additionally, VGG19 has the highest number of parameters with about 143 M (79 times the proposed method). This comparison confirms the efficiency of the proposed framework.