Localization and classification of intracranialhemorrhages in CT data
This work addresses the need for faster diagnosis in acute cases of life-threatening brain injuries, but it appears incremental as it builds on existing CNN methods.
The paper tackled the problem of automatically detecting and classifying intracranial hemorrhages in CT scans, achieving an average Jaccard coefficient of 53.7% on the CQ500 dataset.
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.