## 4 Experimental Results
### Comparison to Existing Methods
#### 4.4.2 Optimization Algorithm Comparison  
This section compares the genetic algorithm with other state-of-the-art methods. For this comparison, a genetic algorithm (GA) [23; 24], a particle swarm optimization algorithm (PSO) [24; 25; 26], a differential evolution algorithm (DE) [24; 27], and a spotted hyena optimizer algorithm (SHO) [42] have been chosen because they use skin lesion images to find the optimal hyperparameters for the CNN model. The performance of the GA, PSO, DE, and SHO have been compared for algorithmic run time, convergence speed, and the performance of skin lesion classification. The genetic algorithm developed here was executed five times to test the speed. For comparison, each of the other optimization algorithms was executed five times to optimize the CNN model hyperparameters [24]. Afterward, the average run time of the five executions was considered the final result. Figure 10 shows the run times of our GA and other state-of-the-art algorithms in finding the optimal hyperparameters. The algorithm developed here was slower than the other three algorithms (Figure 10). This could be for many reasons. First, this algorithm uses an augmented dataset for training and this data contained large samples after the augmentation process. Second, it uses multi-evaluation metrics to find the optimal hyperparameters. However, the genetic algorithm \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **Datasets** & **PREC** & **SN** & **F-Score** & **ACC** & **SP** \\ \hline EDLB [42] & HAM10000 & – & – & – & 91 & – \\ MobileNetV2+LSTM [43] & HAM10000 & – & 92.24 & – & 90.21 & 95.1 \\ DT+RF [44] & HAM10000 & – & – & – & 97.30 & – \\ Conventional+DL [45] & HAM10000 & 94.48 & – & – & 96.7 & – \\ NM-TLN [46] & HAM10000 & – & 90.2 & – & 97.73 & – \\ ELM [47] & HAM10000 & – & – & – & 93.40 & – \\
**Our prop method** & **HAM10000** & **98.86** & **98.57** & **98.71** & **98.66** & **99.23** \\ CNN+TF [48] & ISIC2019 & – & – & – & 96.30 & – \\ GoogleNet+SVM [49] & ISIC2019 & – & – & – & 94.92 & – \\ DenseNet201(Fine-tuned) [50] & ISIC2019 & 88.22 & 80.30 & 84.07 & 91.33 & 98.07 \\ DenseNet201+Cub SVM [50] & ISIC2019 & 84.82 & 92.04 & 86.82 & 92.04 & 96.40 \\ DenseNet201+Fine-KNN [50] & ISIC2019 & 85.22 & 92.75 & 86.96 & 92.75 & 96.38 \\
**Our prop method** & **ISIC2019** & **93.88** & **93.25** & **93.63** & **93.57** & **93.45** \\ MS-SVM [47] & ISIC2018 & 91.52 & – & – & 91.84 & – \\ F-KNN [47] & ISIC2018 & 89.90 & – & – & 90.60 & – \\ ELM [47] & ISIC2018 & 94.08 & – & – & 94.36 & – \\ DLFF [51] & ISIC2018 & – & – & – & 92.40 & – \\ Attenuation-RL [52; 53] & ISIC2018 & – & – & – & 93.40 & – \\
**Our prop method** & **ISIC2018** & **96.00** & **95.86** & **96.00** & **95.96** & **97.45** \\
**Our prop method** & **ISIC2017** & **93.33** & **94.00** & **93.00** & **92.66** & **93.00** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of our method with other state-of-the-art methods in skin lesion recognition (%)developed here performed well in finding optimal hyperparameters and with very good accuracy. In conclusion, this algorithm returns optimal hyperparameters but takes longer to find them compared to other state-of-the-art algorithms. The results of the GA developed here demonstrate its superior accuracy performance compared to the other methods under consideration, albeit with a slower rate of progress (Figure 10). Specifically, beyond two hours, the GA continually outperforms the other methods (Figure 10). Thus, it requires at least double the time but achieves better accuracy. Since the developed GA required more runtime to converge to a better solution, this should lead our work to search strategies or parameters that may need to be refined to perform better within the given runtime constraints. Further investigation will be covered in the future to determine the root cause of the lower output and identify potential avenues for improvement.