Localization and classification of intracranialhemorrhages in CT data
Abstract
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.
- Publication:
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arXiv e-prints
- Pub Date:
- September 2020
- DOI:
- 10.48550/arXiv.2009.03046
- arXiv:
- arXiv:2009.03046
- Bibcode:
- 2020arXiv200903046N
- Keywords:
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- Physics - Medical Physics;
- Computer Science - Computer Vision and Pattern Recognition;
- Electrical Engineering and Systems Science - Image and Video Processing
- E-Print:
- Submitted on EMBEC 2020, paper has not been reviewed yet, 7 pages