  Current coronal holes segmentation methods typically rely on image
thresholding and require non-trivial image pre- and post-processing. We have
trained a neural network that accurately isolates CHs from SDO/AIA 193 Angstrom
solar disk images without additional complicated steps. We compare results with
publicly available catalogues of CHs and demonstrate stability of the neural
network approach. In our opinion, this approach can outperform hand-engineered
solar image analysis and will have a wide application to solar data. In
particular, we investigate long-term variations of CH indices within the solar
cycle 24 and observe increasing of CH areas in about three times from minimal
values in the maximum of the solar cycle to maximal values during the declining
phase of the solar cycle.
