  The morphological classification of radio sources is important to gain a full
understanding of galaxy evolution processes and their relation with local
environmental properties. Furthermore, the complex nature of the problem, its
appeal for citizen scientists and the large data rates generated by existing
and upcoming radio telescopes combine to make the morphological classification
of radio sources an ideal test case for the application of machine learning
techniques. One approach that has shown great promise recently is Convolutional
Neural Networks (CNNs). Literature, however, lacks two major things when it
comes to CNNs and radio galaxy morphological classification. Firstly, a proper
analysis of whether overfitting occurs when training CNNs to perform radio
galaxy morphological classification using a small curated training set is
needed. Secondly, a good comparative study regarding the practical
applicability of the CNN architectures in literature is required. Both of these
shortcomings are addressed in this paper. Multiple performance metrics are used
for the latter comparative study, such as inference time, model complexity,
computational complexity and mean per class accuracy. As part of this study we
also investigate the effect that receptive field, stride length and coverage
has on recognition performance. For the sake of completeness, we also
investigate the recognition performance gains that we can obtain by employing
classification ensembles. A ranking system based upon recognition and
computational performance is proposed. MCRGNet, Radio Galaxy Zoo and ConvXpress
(novel classifier) are the architectures that best balance computational
requirements with recognition performance.
