  We describe our methods that achieved the 3rd and 4th places in tasks 1 and
2, respectively, at ISIC challenge 2019. The goal of this challenge is to
provide the diagnostic for skin cancer using images and meta-data. There are
nine classes in the dataset, nonetheless, one of them is an outlier and is not
present on it. To tackle the challenge, we apply an ensemble of classifiers,
which has 13 convolutional neural networks (CNN), we develop two approaches to
handle the outlier class and we propose a straightforward method to use the
meta-data along with the images. Throughout this report, we detail each
methodology and parameters to make it easy to replicate our work. The results
obtained are in accordance with the previous challenges and the approaches to
detect the outlier class and to address the meta-data seem to be work properly.
