## 4 Performance Validation
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Labels** & **Class** & **No. of Images** \\ \hline C-0 & Angioma & 21 \\ C-1 & Nevus & 46 \\ C-2 & Lentigo NOS & 41 \\ C-3 & Solar Lentigo & 68 \\ C-4 & Melanoma & 51 \\ C-5 & Seborrheic Keratosis & 54 \\ C-6 & BCC & 37 \\ \hline \multicolumn{3}{c}{Total count of Images} & 318 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Details of dataset. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sample Images** & **BSHS-EODL** & **HOCE-ECC [29]** & **GO-FFO [17]** & **GO-PSO [17]** & **GWO [17]** \\ \hline Image 1 & 0.20 & 0.50 & 0.78 & 0.94 & 1.29 \\ Image 2 & 0.13 & 0.31 & 0.51 & 0.70 & 1.05 \\ Image 3 & 0.16 & 0.48 & 0.72 & 0.92 & 1.07 \\ Image 4 & 0.11 & 0.45 & 0.59 & 0.89 & 1.03 \\ Image 5 & 0.15 & 0.41 & 0.68 & 1.00 & 1.31 \\ \hline \multicolumn{3}{c}{PSNR During Attacks (dB)} \\ \hline Sample Images & BSHS-EODL & HOCE-ECC [29] & GO-FFO [17] & GO-PSO [17] & GWO [17] \\ \hline Image 1 & 59.21 & 57.08 & 56.47 & 55.29 & 54.66 \\ Image 2 & 60.17 & 58.34 & 56.90 & 56.01 & 55.48 \\ Image 3 & 58.80 & 57.01 & 55.74 & 54.84 & 54.38 \\ Image 4 & 57.71 & 56.44 & 55.61 & 54.91 & 54.44 \\ Image 5 & 59.31 & 57.43 & 56.59 & 55.42 & 55.09 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: CT and PSNR outcome of BSHS-EODL approach with other techniques under varying images. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sample Images** & **BSHS-EODL** & **HOCE-ECC [29]** & **GO-FFO [17]** & **GO-PSO [17]** & **GWO [17]** \\ \hline Image 1 & 99.42 & 98.93 & 98.49 & 98.16 & 97.7 \\ Image 2 & 99.63 & 99.22 & 98.84 & 98.53 & 98.18 \\ Image 3 & 99.21 & 98.76 & 98.45 & 97.98 & 97.54 \\ Image 4 & 99.72 & 99.33 & 99.01 & 98.65 & 98.19 \\ Image 5 & 99.68 & 99.37 & 99.04 & 98.54 & 98.22 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: CC analysis of BSHS-EODL approach with other techniques under varying images. The confusion matrices of the BSHS-EODL method are investigated under distinct sizes of TRS and TSS in Figure 4. The results demonstrated that the BSHS-EODL technique properly recognizes the samples under every class. In Table 5, the overall results of the BSHS-EODL technique are demonstrated. The results indicate that the BSHS-EODL technique properly classified the images under all classes. For instance, with 80% of TRS, the BSHS-EODL technique reaches average \(accu_{bal}\) of 97.30%. In addition, with 20% of TSS, the BSHS-EODL approach attains average \(accu_{bal}\) of 97.32%. Meanwhile, with 70% of TRS, the BSHS-EODL system attains average \(accu_{bal}\) of 97.68%. Finally, with 30% of TSS, the BSHS-EODL algorithm obtains average \(accu_{bal}\) of 98.51%. Figure 4: Confusion matrices of BSHS-EODL approach (**a,b**) TRS/TSS of 80:20 and (**c,d**) TRS/TSS of 70:30. The training accuracy (TACY) and validation accuracy (VACY) of the BSHS-EODL method can be assessed in Figure 5. The figure inferred that the BSHS-EODL algorithm has demonstrated greater results with enhanced values of TACY and VACY. It can be noted that the BSHS-EODL system has obtained maximal TACY outcomes. The training loss (TLOS) and validation loss (VLOS) of the BSHS-EODL approach are tested in Figure 6. The figure stated that the BSHS-EODL approach has demonstrated higher performance with decreasing TLOS and VLOS. Evidently, the BSHS-EODL algorithm has resulted in lesser VLOS outcomes.