  We provide an algorithm for detection of possible dark matter particle
interactions recorded within NEWSdm detector. The NEWSdm (Nuclear Emulsions for
WIMP Search directional measure) is an underground Direct detection Dark Matter
search experiment. The usage of recent developments in the nuclear emulsions
allows probing new regions in the WIMP parameter space. The directional
approach, which is the key feature of the NEWSdm experiment, gives the unique
chance of overcoming the "neutrino floor". Deep Neural Networks were used for
separation between potential DM signal and various classes of background. In
this paper, we present the usage of deep 3D Convolutional Neural Networks to
take into account the physical peculiarities of the datasets and report the
achievement of the required $10^4$ background rejection power.
