  This work is focused on the morphological classification of galaxies
following the Hubble sequence in which the different classes are arranged in a
hierarchy. The proposed method, BCNN, is composed of two main modules. First, a
convolutional neural network (CNN) is trained with images of the different
classes of galaxies (image augmentation is carried out to balance some
classes); the CNN outputs the probability for each class of the hierarchy, and
its outputs/predictions feed the second module. The second module consists of a
Bayesian network that represents the hierarchy and helps to improve the
prediction accuracy by combining the predictions of the first phase while
maintaining the hierarchical constraint (in a hierarchy, an instance associated
with a node must be associated to all its ancestors), through probabilistic
inference over the Bayesian network so that a consistent prediction is
obtained. Different images from the Hubble telescope have been collected and
labeled by experts, which are used to perform the experiments. The results show
that BCNN performed better than several CNNs in multiple evaluation measures,
reaching the next scores: 67% in exact match, 78% in accuracy, and 83% in
hierarchical F-measure.
