  The universe is composed of galaxies that have diverse shapes. Once the
structure of a galaxy is determined, it is possible to obtain important
information about its formation and evolution. Morphologically classifying
galaxies means cataloging them according to their visual appearance and the
classification is linked to the physical properties of the galaxy. A
morphological classification made through visual inspection is subject to
biases introduced by subjective observations made by human volunteers. For this
reason, systematic, objective and easily reproducible classification of
galaxies has been gaining importance since the astronomer Edwin Hubble created
his famous classification method. In this work, we combine accurate visual
classifications of the Galaxy Zoo project with \emph {Deep Learning} methods.
The goal is to find an efficient technique at human performance level
classification, but in a systematic and automatic way, for classification of
elliptical and spiral galaxies. For this, a neural network model was created
through an Ensemble of four other convolutional models, allowing a greater
accuracy in the classification than what would be obtained with any one
individual. Details of the individual models and improvements made are also
described. The present work is entirely based on the analysis of images (not
parameter tables) from DR1 (www.datalab.noao.edu) of the Southern Photometric
Local Universe Survey (S-PLUS). In terms of classification, we achieved, with
the Ensemble, an accuracy of $\approx 99 \%$ in the test sample (using
pre-trained networks).
