# Skin Lesion Segmentation Method for Dermoscopy Images Using Artificial Bee Colony Algorithm
## 3 Results and Discussion
For SPE, the results of [PERSON] & [PERSON] [38] were the closest. Table 7 and Figure 9 show a comparison between the proposed method and other representative methods that used the ISBI 2017 dataset in terms of the ACC, SEN, SPE, DIC, and JAC. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods** & **SEN** & **SPE** & **ACC** & **DIC** & **JAC** \\ \hline
**Proposed method** & **98.64** & **98.52** & **97.61** & **92.31** & **84.26** \\ [PERSON] et al. [14] & 82.50 & 97.50 & 93.40 & 84.90 & 76.50 \\ [PERSON] et al. [37] & 42.70 & 96.30 & 85.80 & 84.40 & 76.00 \\ [PERSON] \& [PERSON] [38] & 82.00 & 97.80 & 93.20 & 84.70 & 76.20 \\ [PERSON] et al. [21] & 85.40 & 96.69 & 94.03 & 87.08 & 77.11 \\ [PERSON] et al. [22] & 97.50 & 88.80 & 95.30 & 90.38 & 80.32 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparisons of similar studies with the proposed approach using ABC algorithm for the ISBI 2017 dataset. Figure 7: The results presented by the suggestions approach using ABC algorithm compared to existing methods in the literature for the ISBI 2016 dataset. Figure 8: The results presented by the suggestions approach using ABC algorithm compared to existing methods in the literature for the ISBI 2017 dataset. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods** & **SEN** & **SPE** & **ACC** & **DIC** & **JAC** \\ \hline
**Proposed method** & **98.25** & **92.46** & **96.24** & **91.73** & **83.56** \\ [PERSON] et al. [6] & 97.74 & 89.71 & 94.81 & 88.32 & 82.96 \\ [PERSON] \& [PERSON] [39] & 61.60 & 75.80 & 71.70 & & \\ [PERSON] et al. [13] & 91.60 & 89.46 & 86.89 & & \\ [PERSON] \& [PERSON] [23] & 97.40 & 48.40 & 76.90 & & \\ [PERSON] et al. [24] & 87.38 & 90.76 & 82.76 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparisons of similar studies with the proposed approach using ABC algorithm for the DermIS dataset. Figure 9: The results presented by the suggestions approach using ABC algorithm compared to existing methods in the literature for the DermIS dataset. Finally, the proposed method presented better results from the DermIS images in Figure 9 and Table 7 when compared with previous studies. [PERSON] et al. [6] calculated all the parameters, while the remaining researcher calculated SEN, SPE, and ACC only. [PERSON] et al. results were the closest except for in the SPE. Figure 10 shows a comparison examples between the proposed method segmentation results and other representative methods that used the four datasets. We compare a sample image from PH2, ISBI 2016, ISBI 2017, and DermIS datasets with the work of [PERSON] et al. [40] (row 1), [PERSON] et al. [34] (row 2), [PERSON] et al. [22] (row 3), and [PERSON] et al. [6] (row 4). It can be observed that the proposed method surpassed these methods and the difference clear in the skin lesion segmentation border. The ABC method shows fast convergence and low sensitivity to initial conditions. Remarkably, it also needs lower control parameters that lead to reducing the complexity. Moreover, the ABC method is very effective in finding the optimal threshold values. Most studies used in the comparison for all the datasets images are recent studies. Due to uneven bumps in the surrounding skin, such as freckles, dermoscopy image databases could have multiple small objects. By using the median filter, these tiny objects can be filtered out. In some cases, the filter fails to remove all impurities, leading to an effect on algorithm work and results. Figure 11 shows images which have segmentation failure. The images in rows 2 and 3 exhibit similar color intensity between skin lesion and background skin. In this case, it is difficult to determine the optimal threshold value due to color similarities between skin lesion and background and the lesions did not have significant pigmentation.