  We present a Deep-Learning (DL) pipeline developed for the detection and
characterization of astronomical sources within simulated Atacama Large
Millimeter/submillimeter Array (ALMA) data cubes. The pipeline is composed of
six DL models: a Convolutional Autoencoder for source detection within the
spatial domain of the integrated data cubes, a Recurrent Neural Network (RNN)
for denoising and peak detection within the frequency domain, and four Residual
Neural Networks (ResNets) for source characterization. The combination of
spatial and frequency information improves completeness while decreasing
spurious signal detection. To train and test the pipeline, we developed a
simulation algorithm able to generate realistic ALMA observations, i.e. both
sky model and dirty cubes. The algorithm simulates always a central source
surrounded by fainter ones scattered within the cube. Some sources were
spatially superimposed in order to test the pipeline deblending capabilities.
The detection performances of the pipeline were compared to those of other
methods and significant improvements in performances were achieved. Source
morphologies are detected with subpixel accuracies obtaining mean residual
errors of $10^{-3}$ pixel ($0.1$ mas) and $10^{-1}$ mJy/beam on positions and
flux estimations, respectively. Projection angles and flux densities are also
recovered within $10\%$ of the true values for $80\%$ and $73\%$ of all sources
in the test set, respectively. While our pipeline is fine-tuned for ALMA data,
the technique is applicable to other interferometric observatories, as SKA,
LOFAR, VLBI, and VLTI.
