## 4 Performance Validation  
In this section, the experimental outcome of the BSHS-EODL technique is well studied on skin lesion images in the ISIC database. Figure 2 illustrates the sample images. Figure 3 shows the histogram analysis of the BSHS-EODL technique. Figure 3a,b show the original images and their corresponding histogram images. Similarly, Figure 3c,d demonstrate the encrypted images and their respective histograms. Table 1 represents the comparative MSE and PSNR examination of the BSHS-EODL technique with existing techniques [17; 29] on different images. The results indicate that the GWO and GO-PSO algorithms resulted in higher MSE values, while the GO-FFO and HOCE-ECC techniques led to considerable MSE values. However, the BSHS-EODL technique gains effective performance over other models with minimal MSE of 0.072, 0.057, 0.078, 0.102, and 0.071 under test images 1-5, respectively. The experimental outcomes indicate that the GWO and GO-PSO algorithms represented lower PSNR values than existing ones. Although the GO-FFO and HOCE-ECC models reach moderately improved PSNR values, the BSHS-EODL technique outperformed the existing models with a higher PSNR of 59.56 dB, 60.57 dB, 59.21 dB, 58.04 dB, and 59.62 dB under images 1-5 respectively. Figure 2: Sample Images. Table 2 shows a brief CC outcome of the BSHS-EODL system under several images. The experimental outcome stated that the GWO and GO-PSO algorithms represented lesser CC values over existing ones. However, the GO-FFO and HOCE-ECC models reach moderately enhanced CC values, and the BSHS-EODL approach outperformed the existing techniques with maximal CC of 99.42, 99.63, 99.21, 99.72, and 99.68 under images 1-5 correspondingly. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Sample** & \multicolumn{2}{c}{**BSHS-EODL**} & \multicolumn{2}{c}{**HOCE-ECC [29]**} & \multicolumn{2}{c}{**GO-FFO [17]**} & \multicolumn{2}{c}{**GO-PSO [17]**} & \multicolumn{2}{c}{**GWO [17]**} \\ \cline{2-11}
**Images** & **MSE** & **PSNR** & **MSE** & **PSNR** & **MSE** & **PSNR** & **MSE** & **PSNR** & **MSE** & **PSNR** \\ \hline Image 1 & 0.072 & 59.56 & 0.112 & 57.64 & 0.132 & 56.93 & 0.171 & 55.80 & 0.197 & 55.19 \\ Image 2 & 0.057 & 60.57 & 0.083 & 58.94 & 0.119 & 57.38 & 0.152 & 56.31 & 0.172 & 55.78 \\ Image 3 & 0.078 & 59.21 & 0.118 & 57.41 & 0.152 & 56.31 & 0.188 & 55.39 & 0.220 & 54.71 \\ Image 4 & 0.102 & 58.04 & 0.13 & 56.99 & 0.157 & 56.17 & 0.184 & 55.48 & 0.210 & 54.91 \\ Image 5 & 0.071 & 59.62 & 0.107 & 57.84 & 0.128 & 57.06 & 0.165 & 55.96 & 0.186 & 55.44 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: MSE and PSNR analysis BSHS-EODL approach with other systems under varying images. Figure 3: (**a**) Original Images (**b**) Histogram of Original Images (**c**) Encrypted Images (**d**) Histogram of Encrypted Images. Table 3 represents the encryption outcomes of the BSHS-EODL method with existing models under the presence of attacks in terms of computation time (CT) and PSNR. The outcome inferred that the GWO and GO-PSO algorithms resulted in higher CT values, while the GO-FFO and HOCE-ECC approaches led to considerable CT values. The BSHS-EODL algorithm attains effectual performance over other systems with minimal CT of 0.20 s, 0.13 s, 0.16 s, 0.11 s, and 0.15 s under test images 1-5 correspondingly. The experimental outcomes implied that the GWO and GO-PSO algorithms represented minimal PSNR values over existing ones. However, the GO-FFO and HOCE-ECC models reached moderately greater PSNR values, and the BSHS-EODL approach outperformed the existing approaches with enhanced PSNR of 59.21 dB, 60.17 dB, 58.80 dB, 57.71 dB, and 59.31 dB under images 1-5 correspondingly. The classification performance of the BSHS-EODL technique can be tested on the ISIC 2017 Dataset, comprising 318 samples with seven classes, as defined in Table 4.