# Skin Lesion Segmentation Method for Dermoscopy Images Using Artificial Bee Colony Algorithm
## 3 Results and Discussion  
For the evaluation of the performance, the proposed technique was evaluated in four different public datasets images and compared with the results obtained through previous methods, which consisted of finding an automatic global threshold by using the ABC algorithm and then binarized the image to detect the skin lesion. For each image in the four base, the proposed method generated a binomial image (i.e., a segmentation), and this image was then compared with the corresponding ground truth (GT) and the following measurements were computed: accuracy (ACC), sensitivity (SEN), specificity (SPE), dice coefficient (DIC), and Jaccard index (JAC). Accuracy is used to demonstrate the overall pixel with the segmentation analysis. Sensitivity shows the quantity of the skin cancer lesions pixels that are correctly segmented, despite the fact that the SP deals with the ratio of the skin cancer non-lesions pixels that are not correctly segmented. The DIC was applied to quantify the efficiency of the proposed ABC segmentation technique. This measures the ratio of the twice area of the segmented lesions' region intersection with the annotated ground truths to the summation of the segmented lesions region and annotated ground truths. The JAC is an intersection over the union of the segmented skin lesions region with the segment ground truth region [32]; where TP: true positive values, TN: true negative values, FP: false positive values, FN: false negative values, As: segment area, Ag: ground truth area. Table 2 shows the performance parameters that were used in this study. Based on the results presented by the suggestions of the approach using ABC algorithm, good results were observed in most of the datasets performed. From the Table 3, the results of the ISBI 2017 dataset images were slightly better than the results for the other dataset images, with insignificant differences. The average accuracy of the algorithm \begin{table}
\begin{tabular}{c c} \hline \hline
**Measure** & **Formula** \\ \hline Sensitivity & TP/(TP + FN) \\ Specificity & TN/(FP + TN) \\ Accuracy & (TP + TN)/(TP + FP+ TN + FN) \\ Jaccard index & \(\text{Area}\left(\text{A}_{\text{s}}\cap\text{A}_{\text{g}}\right)/\text{Area} \left(\text{A}_{\text{s}}\cup\text{A}_{\text{g}}\right)\) \\ DICE & \(2\text{Area}\left(\text{A}_{\text{s}}\cap\text{A}_{\text{g}}\right)/\text{Area} \left(\text{A}_{\text{s}}+\text{A}_{\text{g}}\right)\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The performance metric (TP: true positive values; TN: true negative values; FP: false positive values; FN: false negative values). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Sensitivity** & **Specificity** & **Accuracy** & **Dice Coefficient** & **Jaccard Index** \\ \hline PH2 & 95.50 \(\pm\) 2.21 & 98.40 \(\pm\) 2.05 & 96.02 \(\pm\) 2.82 & 92.24 \(\pm\) 2.82 & 85.25 \(\pm\) 2.95 \\ ISBI 2016 & 93.42 \(\pm\) 2.45 & 97.31 \(\pm\) 2.52 & 95.24 \(\pm\) 2.38 & 88.51 \(\pm\) 3.03 & 84.61 \(\pm\) 3.12 \\ ISBI 2017 & 98.64 \(\pm\) 1.02 & 98.52 \(\pm\) 1.63 & 97.61 \(\pm\) 2.68 & 92.31 \(\pm\) 2.88 & 84.26 \(\pm\) 3.53 \\ Derms & 98.25 \(\pm\) 1.21 & 92.46 \(\pm\) 2.76 & 96.24 \(\pm\) 2.92 & 91.73 \(\pm\) 2.93 & 83.56 \(\pm\) 4.14 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results for melanoma detection using ABC algorithm (%). was 97.61% successful in detecting the region of the image corresponding to the ground truth region; however, in the ISBI 2016 dataset images, the rate fell to 95.24%. The average accuracy presented in PH2 and ISBI 2016 dataset images show that although the sensitivity was lower, the mean still remained above 95%. However, the Jaccard index still remained above 84%. Figure 4 illustrates various examples of the skin cancer lesions best-segmentation results obtained from the four datasets. We can see 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).