## 5 Conclusions  
Skin cancer is considered as one of the deadliest cancers. It is important to detect skin lesions at a very early stage to control its spread as well as to cure it. In this paper, the different methods were evaluated on an ISIC 2018 skin lesion image dataset. Experimental results on the ISIC 2018 data have shown that the hybrid architecture of TransUNet, combining the properties of transformers and U-Net, outperforms other benchmarking methods both qualitatively and quantitatively in terms of various evaluation metrics. Furthermore, it has been shown that the TransUNet method is robust for various deformations, low contrast and noise. As a future avenue, the results of skin lesion segmentation can be combined with skin lesion classification for further diagnosis of lesion areas of skin in terms of identifying the possible progression of the lesion. Malignant skin lesions are considered fatal, and in order to identify such lesions at the right time, early diagnosis is highly recommended.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Method** & **IoU** & **Dice Coeff** & **Precision** & **Recall** & **Accuracy** \\ \hline
**U-Net [20]** & **80.93** & **82.18** & **86.27** & **98.40** & **87.64** \\ \hline
**V-Net [39]** & 15.95 & 17.31 & 21.52 & 52.04 & 28.02 \\ \hline
**Attention** & **81.21** & **83.27** & **86.75** & **98.71** & **88.74** \\ \hline
**TransUNet** [41]** & **86.72** & **89.13** & **89.44** & **99.02** & **91.02** \\ \hline
**Swin-UNet** [42] & 13.25 & 17.12 & 14.32 & 21.20 & 14.58 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparative results of the different methods on train set.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Method** & **Training Time** & **Inference Time** & **Dataset Limit** \\  & **(s)** & **(s)** & **Dataset Limit** \\ \hline
**U-Net [20]** & 24,588 & 12 & 2594 \\ \hline
**V-Net [39]** & 1172 & 6 & 2594 \\ \hline
**Attention U-Net [40]** & 34,380 & 15 & 2594 \\ \hline
**TransUNet [41]** & 77,976 & 38 & 2594 \\ \hline
**Swin-UNet [42]** & 62,568 & 27 & 2594 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparative results in terms of training time, inference time and dataset limit.  
For that reason, incorporating the clinical knowledge into the lesion image identification system to automatically analyse and diagnose the current state of skin lesions with a high accuracy rate is crucial.  
Conceptualization, Y.G. and S.A.K.; Data curation, S.A.K.; Formal analysis, S.A.K.; Funding acquisition, Y.G.; Investigation, S.A.K.; Methodology, Y.G. and S.A.K.; Project administration, Y.G.; Resources, Y.G. and S.A.K.; Software, S.A.K.; Supervision, Y.G. and S.A.K.; Validation, Y.G. and S.A.K.; Visualization, Y.G. and S.A.K.; Writing--original draft, Y.G. and S.A.K.; Writing--review & editing, Y.G. and S.A.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Deanship of Scientific Research, Vice Presidency for Graduate Studies and Scientific Research, King Faisal University, Saudi Arabia, Project No. GRANT382.  
Not applicable.  
The dataset used in this study is a public dataset (ISIC 2018 Dataset [13]).  
The authors declare no conflict of interest. The funder had no role in the design of the study, in the writing of the manuscript or in the decision to publish the results.