In Brazil, skin cancer has become the most frequent neoplasm
among patients. This type of cancer, if detected early, increases the

chances of cure. However, with the lack of health professionals qual-
ified to perform this procedure, for example, in distant regions large

urban centers, difficulties in early diagnosis process are recurring.

Thus, a possible solution to this problem is the development of mod-
els that allow classification for the diagnosis skin based on Deep

Learning. Therefore, this work presents a model that can contribute
to the diagnosis of types of skin devices in the HAM10000 dataset.
The proposed model achieved a balanced accuracy of 74.50% with
a set.