  The morphological classification of galaxies is a relevant probe for galaxy
evolution and unveils its connection with cosmological structure formation. To
this scope, it is fundamental to recover galaxy morphologies over large areas
of the sky. In this paper, we present a morphological catalogue for galaxies in
the Stripe-82 area, observed with S-PLUS, till a magnitude limit of $r\le17$,
using the state-of-the-art of Convolutional Neural Networks (CNNs) for computer
vision. This analysis will then be extended to the whole S-PLUS survey data,
covering $\simeq 9300$ $deg^{2}$ of the celestial sphere in twelve optical
bands. We find that the network's performance increases with 5 broad bands and
additional 3 narrow bands compared to our baseline with 3 bands. However, it
does lose performance when using the full $12$ band image information.
Nevertheless, the best result is achieved with 3 bands, when using pre-trained
network weights in an ImageNet dataset. These results enhance the importance of
previous knowledge in the neural network weights based on training in non
related extensive datasets. Thus, we release a model pre-trained in several
bands that could be adapted to other surveys. We develop a catalogue of 3274
galaxies in Stripe-82 that are not presented in Galaxy Zoo 1 (GZ1). We also add
classification to 4686 galaxies considered ambiguous in GZ1 dataset. Finally,
we present a prospect of a novel way to take advantage of $12$ bands
information for morphological classification using multiband morphometric
features. The morphological catalogues are publicly available.
