  In this paper we introduce a reliable, fully automated and fast algorithm to
detect extended extragalactic radio sources (cluster of galaxies, filaments) in
existing and forthcoming surveys (like LOFAR and SKA). The proposed solution is
based on the adoption of a Deep Learning approach, more specifically a
Convolutional Neural Network, that proved to perform outstandingly in the
processing, recognition and classification of images. The challenge, in the
case of radio interferometric data, is the presence of noise and the lack of a
sufficiently large number of labeled images for the training. We have
specifically addressed these problems and the resulting software, COSMODEEP
proved to be an accurate, efficient and effective solution for detecting very
faint sources in the simulated radio images. We present the comparison with
standard source finding techniques, and discuss advantages and limitations of
our new approach.
