## 5 Results and Discussion
### Protocol II  
For the purpose of parameter tuning with more test images, the following protocol-II is chosen where the dataset is split into the following ratio ((Train + Val):Test) = ((90 + 10):20). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Lesion Class** & **Precision** & **Recall** & **F1-Score** \\ \hline
0-akiec & 1.00 & 1.00 & 1.00 \\ \hline
1-bcc & 0.99 & 1.00 & 0.99 \\ \hline
2-bkl & 0.97 & 1.00 & 0.99 \\ \hline
3-df & 1.00 & 1.00 & 1.00 \\ \hline
4-nv & 1.00 & 0.92 & 0.96 \\ \hline
5-vasc & 1.00 & 1.00 & 1.00 \\ \hline
6-mel & 0.96 & 1.00 & 0.98 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Class-wise performance measures of the model. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sr. No.** & **Work** &
\begin{tabular}{c} **Data Augmentation/Balancing? (Yes/No).** \\ **Total Number of Images after Data** \\ **Augmentation/Balancing** \\ \end{tabular} & **Methodology** & **Accuracy (\%)** \\ \hline  & & & InceptionV3 & 85.80\% \\ \cline{3-4}
1 & \multirow{2}{*}{
\begin{tabular}{c} [PERSON] et al. [37] \\ \end{tabular} } & Yes, & ResNet50 & 86.69\% \\ \cline{3-4}  & & & DenseNet201 & 86.91\% \\ \hline
2 & \multirow{2}{*}{
\begin{tabular}{c} [PERSON] et al. [14] \\ \end{tabular} } & Yes, & Grouping of Multi-scale Attention & \multirow{2}{*}{91.6\%} \\ Not mentioned & & Blocks (GMAB) & \\ \hline
3 & \multirow{2}{*}{
\begin{tabular}{c} [PERSON] et al. [8] \\ \end{tabular} } & Yes, & 1400 & Convolutional neural network (CNN) & 95.18\% \\ \hline
4 & \multirow{2}{*}{
\begin{tabular}{c} [PERSON] et al. [16] \\ \end{tabular} } & No & Concatenated Xception-ResNet50 - & 97.8\% \\ \hline
5 & Proposed algorithm &
\begin{tabular}{c} Yes, \\ 46,935 \\ \end{tabular} & ESRGAN-CNN & 98.77\% \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Highest Accuracy for protocol-I. Figure 6: Confusion matrix for protocol-I (80:20 Train:Test split). It indicates that at first, the dataset is divided into 80: 20 Train: Test split. Subsequently, the training set is subdivided into 90% for training and 10% for validation. For this augmented data the number of training images is 33,793, validation images is 3755 as well as 9387 test images. Class-wise samples for this experimentation are depicted in Table 9. The model was trained using a machine with 12 GB RAM and GPU attached to it. Training and validation accuracies and losses are indicated in Figures 7 and 8, respectively. The confusion matrix for protocol-II is indicated in Figure 9.  
Various Approaches That Follow Protocol-II  
Table 10 specifies accuracies in the context of current research performed on the HAM10000 database. In [PERSON] et al. (2019), a deep convolutional neural network was implemented for the classification of skin lesions. This study accomplished an accuracy of 91.51%. [PERSON] et al. (2018) worked on the HAM10000 dataset for skin-lesion classification. In this study, the robustness, stability, and fidelity studies of the deep convolutional neural network are carried out. Their model gives a classification accuracy of 80%. The proposed method for protocol-II gives a better accuracy of 98.36% as indicated in Table 10. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Class** & **akiec** & **bcc** & **bkl** & **df** & **nv** & **vasc** & **mel** \\ \hline Training Samples & 4845 & 4817 & 4867 & 4875 & 4792 & 4807 & 4790 \\ \hline Validation Samples & 538 & 535 & 541 & 542 & 533 & 534 & 532 \\ \hline Testing Samples & 1322 & 1353 & 1297 & 1288 & 1380 & 1364 & 1383 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Class-wise images present in the HAM10000 dataset with protocol-II. Figure 7: Accuracy graph for training and testing for protocol-II. Figure 8: Training and testing losses for protocol-II. Figure 9: Confusion matrix for protocol-II. Various Approaches That Follow Protocol-III  
Table 12 specifies accuracies, in the context of current research performed on the HAM10000 database. The research article presented by [PERSON] et al. [2] focused on kernel  
Figure 11: Training and testing losses for Protocol-III.