## 5 Conclusions  
Skin cancer is one of the most dangerous diseases in humans. The automated classification of skin lesions using DL will save time for physicians and increase the curing rate. Typical DL frameworks require high parameters and cannot work on the mobile system. Hence, developing the lightweight DL framework for skin lesion classification is essential. In this paper, we propose a novel method for skin lesion classification. Our lightweight method improves the limitation of evaluating a small number of skin lesion images in the past. The numerical results show that the proposed framework is more efficient and accurate than the 20 other approaches (see Table 3). The proposed method reduces the number of the parameters to approximately 79 times that of another method (VGG19) while maintaining higher accuracy. Additionally, the proposed method achieves higher accuracy than other non-segmentation and non-DL segmentation methods and the approximate results at the level of the DL segmentation methods while reducing the complexity of the DL segmentation methods. Our framework does not require ground truth for image segmentation, whereas DL segmentation methods cannot work without ground truth. Thus, the proposed method decreases the effort of the dermatologists to manually outline the ground truth pixel-wise segmentation. We create an accurate and efficient framework by combining the new EW-FCM segmentation technique and wide-ShuffleNet. We will compare the proposed framework with more networks in future work. Another future direction for research and development is to integrate the proposed method into real-world problems like the mobile healthcare system. \begin{table}
\begin{tabular}{c c c c c} \hline
**Experiment** & **Method** & **ACC** & **Sensitivity** & **Specificity** \\ \hline \multirow{4}{*}{Experiment 1} & Original Image + ShuffleNet & 76.83\% & 76.83\% & 96.14\% \\  & Original Image + wide-ShuffleNet & 77.88\% & 77.88\% & 96.31\% \\ \cline{2-5}  & \multirow{2}{*}{EW-FCM + ShuffleNet} & 83.66\% & 83.66\% & 97.28\% \\  & EW-FCM + wide-ShuffleNet & 84.80\% & 84.80\% & 97.48\% \\  & EW-FCM + EfficientNet-B0 & 85.50\% & 85.50\% & 97.58\% \\ \hline \multirow{4}{*}{Experiment 2} & Original Image + ShuffleNet & 77.25\% & 77.25\% & 96.21\% \\  & Original Image + wide-ShuffleNet & 78.64\% & 78.64\% & 96.44\% \\ \cline{2-5}  & \multirow{2}{*}{EW-FCM + ShuffleNet} & 84.43\% & 84.43\% & 97.41\% \\ \cline{2-5}  & EW-FCM + wide-ShuffleNet & 86.33\% & 86.33\% & 97.72\% \\ \cline{2-5}  & EW-FCM + EfficientNet-B0 & 87.23\% & 87.23\% & 97.87\% \\ \hline \multirow{4}{*}{Experiment 3} & Original Image + ShuffleNet & 74.23\% & 74.23\% & 96.32\% \\  & Original Image + wide-ShuffleNet & 75.73\% & 75.73\% & 96.53\% \\ \cline{1-1} \cline{2-5}  & EW-FCM + ShuffleNet & 81.62\% & 81.62\% & 97.38\% \\ \cline{1-1} \cline{2-5}  & EW-FCM + wide-ShuffleNet & 82.56\% & 82.56\% & 97.51\% \\ \cline{1-1} \cline{2-5}  & EW-FCM + EfficientNet-B0 & 84.66\% & 84.66\% & 97.81\% \\ \hline \end{tabular}
\end{table} 
Table 8: Comparison with different networks on the HAM10000 and ISIC2019 datasets (micro-average). **Author Contributions:** Conceptualization, L.H. ; Funding acquisition, S.-H.L. and K.-R.K.; Investigation, L.H. ; Methodology, L.H. ; Project administration, S.-H.L. and K.-R.K.; Software, L.H. ; S.-H.L. and K.-R.K.; Supervision, S.-H.L., E.-J.L., and K.-R.K.; Validation, S.-H.L. and K.-R.K.; Writing--original draft, L.H. ; Writing--review and editing, L.H. and S.-H.L. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by the Brain Korea 21 project (BK21). **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable.