Publication detail

Detection of intracranial haemorrhages in head CT data based on deep learning

NEMČEK, J.

Original Title

Detection of intracranial haemorrhages in head CT data based on deep learning

English Title

Detection of intracranial haemorrhages in head CT data based on deep learning

Type

conference paper

Language

en

Original 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 im-age. The accuracies of the three models are 90.19%, 88.15%, and 80.90% for the axial, sagital and coronal plane.

English 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 im-age. The accuracies of the three models are 90.19%, 88.15%, and 80.90% for the axial, sagital and coronal plane.

Keywords

Intracranial haemorrhage, CT, classification, detection, convolutional neural network

Released

23.04.2020

Publisher

Brno University of Technolog, Faculty of Electrical Engineering anf Communication

Location

Brno

ISBN

978-80-214-5868-0

Book

Proceedings II of the 26th Conference STUDENT EEICT 2020

Pages from

72

Pages to

75

Pages count

4

URL

Documents

BibTex


@inproceedings{BUT164865,
  author="Jakub {Nemček}",
  title="Detection of intracranial haemorrhages in head CT data based on deep learning",
  annote="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 im-age. The accuracies of the three models are 90.19%, 88.15%, and 80.90% for the axial, sagital and coronal plane.",
  address="Brno University of Technolog, Faculty of Electrical Engineering anf Communication",
  booktitle="Proceedings II of the 26th Conference STUDENT EEICT 2020",
  chapter="164865",
  howpublished="online",
  institution="Brno University of Technolog, Faculty of Electrical Engineering anf Communication",
  year="2020",
  month="april",
  pages="72--75",
  publisher="Brno University of Technolog, Faculty of Electrical Engineering anf Communication",
  type="conference paper"
}