Detection Of Intracranial Haemorrhages In Head Ct Data Based On Deep Learning
| but.event.date | 23.04.2020 | cs |
| but.event.title | Student EEICT 2020 | cs |
| dc.contributor.author | Nemček, Jakub | |
| dc.date.accessioned | 2021-07-15T13:12:38Z | |
| dc.date.available | 2021-07-15T13:12:38Z | |
| dc.date.issued | 2020 | cs |
| dc.description.abstract | In this paper, we present a method for detection of intracranial haemorrhages in the head CT data using convolutional neural networks. We introduce three 2D image classifiers that perform in three perpendicular anatomical planes and classify the CT slices into healthy or pathological, whereby they provide the information about the position of the haemorrhage in the 3D CT image. The accuracies of the three models are 90.19%, 88.15%, and 80.90% for the axial, sagittal and coronal plane. | en |
| dc.format | text | cs |
| dc.format.extent | 72-75 | cs |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.citation | Proceedings II of the 26st Conference STUDENT EEICT 2020: Selected Papers. s. 72-75. ISBN 978-80-214-5868-0 | cs |
| dc.identifier.isbn | 978-80-214-5868-0 | |
| dc.identifier.uri | http://hdl.handle.net/11012/200623 | |
| dc.language.iso | en | cs |
| dc.publisher | Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií | cs |
| dc.relation.ispartof | Proceedings II of the 26st Conference STUDENT EEICT 2020: Selected papers | en |
| dc.relation.uri | https://conf.feec.vutbr.cz/eeict/EEICT2020 | cs |
| dc.rights | © Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií | cs |
| dc.rights.access | openAccess | en |
| dc.subject | Intracranial haemorrhage | en |
| dc.subject | CT | en |
| dc.subject | classification | en |
| dc.subject | detection | en |
| dc.subject | convolutional neural network | en |
| dc.title | Detection Of Intracranial Haemorrhages In Head Ct Data Based On Deep Learning | en |
| dc.type.driver | conferenceObject | en |
| dc.type.status | Peer-reviewed | en |
| dc.type.version | publishedVersion | en |
| eprints.affiliatedInstitution.department | Fakulta elektrotechniky a komunikačních technologií | cs |
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