# Skin Lesion Segmentation Method for Dermoscopy Images Using Artificial Bee Colony Algorithm
## 3 Results and Discussion
Figure 5 illustrates the results obtained from the four datasets with images showing large variations in color, edges, and shape. The efficiency of the proposed approach when compared to the images obtained by the proposed approach (green) with the images of the ideal-ground-truth (white) can be seen. To show the robustness of this work, the results were compared to existing methods in the literature for melanoma detection. Table 4 and Figure 6 show a comparison between the proposed method and other representative methods that used PH2 dataset in terms of ACC, SEN, SPE, DIC, and JAC. Figure 4: Various examples of skin cancer lesions’ segmentation results for a different dataset: Row (**a–d**) PH2, ISBI 2016, ISBI 2017, and DermIs datasets. (White: ground truth, green: segmented area). (Scale bar: 100 μm). According to Figure 6 and Table 4 above, it can be observed that the best-segmented result from the PH2 images was that of the proposed method by better delineating the border region of the lesion and better performance parameters. Also, it can be observed that the closer best-segmented result was that of the [PERSON] et al. [21] in terms of ACC, DIC, and JAC. For SPE and SEN, the results of [PERSON] et al. [32] and [PERSON] et al. [33] were the closest. Although [PERSON] et al. did not calculate ACC, DIC, and JAC in their work. Table 5 and Figure 7 show a comparison between the proposed method and other representative methods that used ISBI 2016 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** & **95.50** & **98.40** & **96.02** & **92.24** & **85.25** \\ [PERSON] et al. [32] & 90.40 & 97.00 & 92.80 & 90.00 & 83.70 \\ [PERSON] et al. [21] & 93.72 & 95.65 & 95.08 & 91.77 & 84.79 \\ [PERSON] et al. [20] & 92.50 & 76.30 & 84.30 & & \\ [PERSON] et al. [33] & 94.00 & 92.00 & & & \\ [PERSON] et al. [26] & 83.30 & 95.00 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparisons of similar studies with the proposed approach using ABC algorithm for the PH2 dataset. Figure 5: Detection results for skin cancer lesions: Row (1–4) PH2, ISBI 2016, ISBI 2017, and DermIS datasets; Columns (**a**) multiple colored lesions; (**b**) specular reflections; (**c**) presence of hair; and (**d**) presence of bubbles. (White: ground truth, green: segmented area). (Scale bar: 100 \(\upmu\)m). Figure 7 and Table 5 above show that the best-segmented result from ISBI 2016 images was that of the proposed method by better delineating the border region of the lesion and better performance parameters. Also, it can be observed that the closer best-segmented result was that of [PERSON] et al. [27] in terms of SEN and SPE. For DIC, ACC, and JAC, the result of [PERSON] et al. [34] was the closer. Table 6 and Figure 8 show a comparison between the proposed method and other representative methods that used 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** & **93.42** & **97.31** & **95.24** & **88.51** & **84.61** \\ [PERSON] et al. [34] & 91.10 & 95.70 & 94.9 & 87.7 & 82.9 \\ [PERSON] et al. [35] & 69.30 & 83.60 & 80.70 & & \\ [PERSON] et al. [25] & 47.60 & 88.10 & 79.20 & & \\ [PERSON] et al. & & & & & \\
[36] & 74.60 & 84.50 & 82.50 & & \\ [PERSON] et al. [27] & 91.80 & 96.70 & & 27.70 & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparisons of similar studies with the proposed approach using ABC algorithm for the ISBI 2016 dataset. Figure 6: The results presented by the suggestions approach using ABC algorithm compared to existing methods in the literature for the PH2 dataset. Also, in Figure 8 and Table 6 above, the proposed method achieved the best-segmented result from the ISBI 2017 images. It can be observed that the closer best-segmented result was that of the [PERSON] et al. [22] in terms of SEN, ACC, DIC, and JAC.