## 5 Conclusions  
In this paper, two analyses have been performed. Firstly, a comparison between an unsupervised segmentation method and two supervised segmentation methods, based \begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & \multicolumn{2}{c}{**Winner Challenge 2019 [46]**} & \multicolumn{3}{c}{**Proposed Method**} \\ \cline{2-6}  & **Se** & **Sp** & **Se** & **Sp** & **Acc** \\ \hline AK & 0.48 & 0.97 & 0.48 & 1.00 & 0.96 \\ BCC & 0.72 & 0.94 & 0.83 & 0.98 & 0.96 \\ BKL & 0.39 & 0.99 & 0.50 & 0.99 & 0.94 \\ DF & 0.58 & 0.98 & 0.48 & 1.00 & 0.99 \\ MEL & 0.59 & 0.96 & 0.61 & 0.97 & 0.91 \\ NEVUS & 0.71 & 0.98 & 0.97 & 0.71 & 0.84 \\ SCC & 0.44 & 0.99 & 0.43 & 1.00 & 0.98 \\ VASC & 0.64 & 0.99 & 0.79 & 1.00 & 1.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance parameters for the winner of the ISIC Challenge 2019 [46] and for the proposed method (Argmax ensemble without previous segmentation) when classifying into eight types of lesions. Se: Sensitivity, Sp: Specificity, Acc: Accuracy. on deep learning, has been carried out. Secondly, the identification of BCC amongst other types of skin lesions has been performed in the following two different scenarios: with a previous segmentation of the lesion and without segmenting the lesion. To this second task, different deep neural networks have been tested. Experiments to compare the different segmentation methods show that SegNet architecture has attained the best behavior, obtaining 94% accuracy. The ISIC 2019 public database [6] has been used to carry out the classification task. A 98% accuracy, 0.84% sensitivity and 0.96% specificity, for distinguishing BCC from nevus, and a 95% accuracy, 0.68% sensitivity and 0.97% specificity, classifying BCC vs. all lesions, have been obtained. Furthermore, the proposed algorithm outperforms the winner of the ISIC 2019 challenge in almost all the metrics, when lesions are classified into eight classes. In summary, this paper adds important comparison studies, applied to the analysis of BCC, that have not been performed previously. These studies are of interest, because BCC is the skin cancer of highest incidence. First, an analysis of the utility of BCC segmentation to improve classification is carried out, driving to the conclusion that previous segmentation does not improve the classification. Secondly, a tool for the discrimination between BCC and nevus, which is the most common pigmented lesion, is provided. Finally, we have demonstrated that an ensemble of well-known CNN can attain results that can compete with the best methods in the ISIC challenge. Conceptualization, P.V., C.S. and B.A. ; methodology, P.V. and M.M. ; software, P.V. and M.M. ; validation, P.V., M.M., C.S. and B.A. ; resources, C.S. and B.A. ; writing--original draft preparation, P.V. ; writing--review and editing, C.S. and B.A. ; visualization, P.V. ; supervision, C.S. and B.A. ; funding acquisition, C.S. and B.A. All authors have read and agreed to the published version of the manuscript. This work was funded by Project DPI2016-81103-R (\"Ministerio de Economia y Competitividad\", Spanish Government) and Project US-1381640 (FEDER-US, Regional Government of Andalusia). [PERSON] has been hired by \"Fondo Social Europeo Iniciativa de Empleo Juvenil\" 2019-3-EJ3-83-1 (European Union and Andalusian Government). Databases employed in this study are available at [[https://challenge.isic-archive.com/data/](https://challenge.isic-archive.com/data/)]([https://challenge.isic-archive.com/data/](https://challenge.isic-archive.com/data/)) (accessed on 30 December 2021). The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.