# Skin Lesion Segmentation in Dermoscopic Images with Combination of YOLO and GrabCut Algorithm
## 5 Discussion  
Automatic segmentation of the skin lesions is a very important step for CAD systems in the classification of skin lesions as melanoma. Especially as deep learning-based methods need a huge amount of data for proper segmentation. No data augmentation methods were employed in this study. [PERSON] was trained only with 2000 images and validated with 150 images. The test results show that the proposed pipeline method attained promising results when compared to other deep learning-based approaches. The main advantage of the proposed method is that this pipeline can segment skin lesions using high spatial resolution images. Therefore, more detailed features can be obtained from the segmented part of the lesion, which increases the classification accuracy. The comparison of the robustness of the proposed method with that of state-of-the art methods is shown in Table 4. This table presents the performance comparison of our method with approaches including U-Net based method [54], the most successful three techniques in the ISBI 2017 Challenge [55; 76; 77], and a study named FrCN [56].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**References** & **Acc** & **Sen** & **Spe** & **Jac** & **Dic** \\ \hline [PERSON] et al. (CDNN) [55] & 93.40 & 82.50 & 97.50 & 76.50 & 84.90 \\ [PERSON] et al. [76] & 93.20 & 82.00 & 97.80 & 76.20 & 84.70 \\ [PERSON] et al. (ResNets) [77] & 93.40 & 80.20 & 98.50 & 76.00 & 84.40 \\ [PERSON] et al. (U-Net) [54] & - & - & - & 62.00 & 77.00 \\ [PERSON] et. al. [56] & 94.03 & 85.40 & 96.69 & 77.11 & 87.08 \\ Proposed Method & 93.39 & **90.82** & 92.68 & 74.81 & 84.26 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The comparison of the proposed method skin lesion segmentation performance (%) with the latest studies in the literature.  
Figure 13: Skin lesion segmentation results of the proposed method. (**a**) Original image, (**b**) lesion location detection by Yolov3, (**c**) ground truth, (**d**) segmented lesion area by GrabCut and, (**e**) the final result.  
According to the results, the proposed method outperformed other methods in sensitivity with 90.82 %. It also attained higher performances at Jac and Dic (74.81% and 84.16%) than the method proposed by [PERSON] et al. When the other results are examined, it is obvious that there is not a significant difference between our proposed method and the others. Our proposed segmentation pipeline method gives a less perfect segmentation of lesion than the other deep learning-based methods. However, this situation is an advantage according to a study [78] claiming that the surrounding border of the skin lesion has beneficial information in the classification of lesions. On the other hand, modified Yolov3 used in this study achieved 90% and 86% IOU rates in the detection of lesion location on the PH2 and the ISBI 2017 datasets respectively. Figure 12 shows the modified Yolov3 does not detect the lesion because of the similarity between the lesion and surrounding tissue in the images with low contrast, and in the images where the lesion occupies the entire image surface. This problem can be overcome with more training data and contrast enhancement. Additionally, the segmentation processing time of the presented pipeline model is almost 7 s, which suggests that the method is feasible for real-time medical practices.