## 5 Results and Discussion
### Protocol-I (Train:Test = 80:20 Ratio)  
After performing data augmentation, the entire dataset is split into two partitions, namely, train and test with the ratio of 80:20. For protocol-I, for this augmented data, the total number of training images is 37,548 and of test images is 9387. The details of class-wise training and test images are depicted in Table 6.  
The model was trained for 25 epochs on the Google Colaboratory Pro platform with 12 GB RAM and Python 3 Google compute backend engine GPU Accelerator. We interrupt the model's continuing execution using the early stopping method and record the model's best-performing parameters, such as its maximum accuracy and minimum cross-entropy loss. Every time the model fails to reach an accuracy greater than those acquired in the previous two epochs, we decreased the learning rate of the model to prevent additional stalling in the learning phase. The training and testing accuracies and loss graphs are displayed in Figures 4 and 5, respectively. The highest testing accuracy was obtained as 98.77% on the 25 th epoch. Accuracy is one of the important metrics to characterize the achievement of the model if the dataset is proportionate. To get the different evaluation scores, we have used the confusion matrix which gives the exact classifications as shown in Figure 6. In this experiment, a confusion matrix is incurred for seven classes as mentioned in the dataset. The confusion matrix scores are computed to examine the performance of the model for different classes. From Table 7, it can be observed that the model works very well in classifying class 0, class 3, and class 5. The scores obtained for class 4 are slightly low. Various Approaches That Follow Protocol-I  
Table 8 specifies accuracies in the context of current research performed on the HAM10000 database. In the [PERSON] et al. [37], the authors carried out research work on the HAM10000 database. Transfer learning techniques like InceptionV3, ResNet50, DenseNet201, and comparative study is accomplished on the HAM1000 database and achieved accuracies of 85.80%, 86.69%, and 86.91%, respectively. The authors have reached the highest accuracy for the DenseNet201 model. In another work by [PERSON] et al. in [19], the presented approach included a custom CNN model and experiments with an image size of 75 \(\times\) 100. An accuracy of 91.51% was achieved in this study. [PERSON] et al. [14] presented an experimental study using the CNN model concatenated with the Grouping Of Multi-Scale Attention Blocks (GMAB) technique. This study achieved an accuracy of 91.6 %. [PERSON] \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Class** & **akiec** & **bcc** & **bkl** & **df** & **nv** & **vasc** & **mel** \\ \hline Training Samples & 5383 & 5352 & 5408 & 5417 & 5325 & 5341 & 5322 \\ \hline Testing Samples & 1322 & 1353 & 1297 & 1288 & 1380 & 1364 & 1383 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Class-wise images present in HAM10000 dataset with protocol-I. \begin{table}
\begin{tabular}{c c} \hline \hline
**Performance Metrics** & **Formula** & **Equation** \\ \hline Accuracy & \(\frac{(T_{p}+T_{p})}{(T_{p}+T_{p}+T_{p}+T_{p})}\) & (7) \\ \hline F1-Score & \(\frac{(2*Precision*Recall)}{(Precision+Recall)}\) & (8) \\ \hline Recall & \(\frac{T_{p}}{(T_{p}+T_{p})}\) & (9) \\ \hline Precision & \(\frac{T_{p}}{T_{p}+T_{p}}\) & (10) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance metrics and their formulas. et al. [8] developed a CNN model along with a k-fold cross-validation method. The accuracy of this model was 95.18%. The study carried out by [PERSON] et al. [16], and was based on the Concatenated Xception-ResNet50 model for the diagnosis of skin cancer. This model yields competitive results with an accuracy of 97.8%. The proposed work presents a custom CNN model and implements it on pre-processed data using the ESRGAN algorithm to achieve an accuracy of 98.77% which is much higher when compared to other literature studies carried out on the HAM10000 database.