  Intracranial hemorrhages (ICHs) are life-threatening brain injures with a
relatively high incidence. In this paper, the automatic algorithm for the
detection and classification of ICHs, including localization, is present. The
set of binary convolutional neural network-based classifiers with a designed
cascade-parallel architecture is used. This automatic system may lead to a
distinct decrease in the diagnostic process's duration in acute cases. An
average Jaccard coefficient of 53.7 % is achieved on the data from the publicly
available head CT dataset CQ500.
