## 4 Experimental Results
### Comparison to Existing Methods  
#### 4.4.1 Skin Lesion Classification Model Comparison  
Based on the results presented in Table 7, our method outperformed the other classification methods in terms of precision, sensitivity, F-score, accuracy, and specificity. The method reached a precision of 98.86%, sensitivity of 98.57%, F-score of 98.71%, accuracy of 98.66% and specificity of 99.23% on the HAM10000 dataset. The following best method in terms of accuracy is the NM+TLN method, which scored 97.73%. The NM+TLN method only used precision and accuracy for evaluation, it would require more work to compare its performance with our method in terms of sensitivity, F-score, and specificity. However, based on the presented results, our method outperforms NM+TLN in terms of precision and accuracy. The MobileNetV2+LSTM method reached a reasonable specificity of 95.01%, but the accuracy and specificity are lower than our method. The Conventional+DL method has the highest accuracy of the other methods, but its accuracy is still lower than our method. The ELM method performed reasonably well, with an accuracy of 93.40%, but this is still less than for the method developed here.  
From the ISIC2019 dataset, the method proposed here for skin lesion classification is the overall best performer among the methods compared (Table 7). It demonstrates superior performance across the various evaluation metrics, including precision, sensitivity, F-score, accuracy, and specificity. The CNN+TF method scored higher accuracy, but it only uses this metric. Likewise, the GoogleNet+SVM method had better accuracy and all the methods that used specificity did better for this measure. However, these methods fall short of our method in overall performance.  
The ISIC2018 dataset was also used to evaluate the various skin lesion classification methods to the method developed here (Table 7). Our method performed best among the methods compared. It demonstrated exceptional performance across all the evaluation metrics, including precision, sensitivity, F-score, accuracy, and specificity. Our developed model also performed well in evaluating skin lesion classification methods using the ISIC2017 dataset, as shown in Table 7.  
Figure 9: The incorrect values for each class using the HAM10000, ISIC2018 and ISIC2019, ISIC2017 datasets.  
Overall, in comparison to existing methods, the result shows our method performed well for skin lesion classification. The method proposed here could provide a comprehensive and effective solution for the task considered. Generally, the high precision, sensitivity, and specificity values make it a reliable and trustworthy tool. Furthermore, compared to widely used CNN architectures like VGG16, AlexNet, and ResNet, which have over a million trainable parameters, our optimized model has significantly fewer parameters at 384,455. Together with the fact that our method is shown here to be functional, much fewer parameters are required to reduce the computational cost and memory requirements during training, which minimizes computation time overall. A smaller number of parameters also means a more compact model size, making it easier to deploy on edge devices or in resource-constrained environments. In comparison, the method developed here has fewer layers than most standard CNN architectures of layers at a better computational cost, consisting of 13 layers, compared to the standard architectures, which typically have more than 13 layers.