Publication detail

DETECTION OF PATHOLOGICAL VERTEBRAE IN SPINAL CT UTILISED BY MACHINE LEARNING METHODS

TYSHCHENKO, B.

Original Title

DETECTION OF PATHOLOGICAL VERTEBRAE IN SPINAL CT UTILISED BY MACHINE LEARNING METHODS

English Title

DETECTION OF PATHOLOGICAL VERTEBRAE IN SPINAL CT UTILISED BY MACHINE LEARNING METHODS

Type

conference paper

Language

Czech

Original Abstract

This paper presents a computer aided detection system to identify pathological vertebrae and to classify a type of pathology. Designed classification system is based on using neural network (NN), which performs classification step and on principal component analysis (PCA), which is used to reducing the original number of observation features.

English abstract

This paper presents a computer aided detection system to identify pathological vertebrae and to classify a type of pathology. Designed classification system is based on using neural network (NN), which performs classification step and on principal component analysis (PCA), which is used to reducing the original number of observation features.

Keywords

Neural network; Classification; CT; Machine Learning; Pathologies of spine; Principal Component Analysis; Vertebra

Key words in English

Neural network; Classification; CT; Machine Learning; Pathologies of spine; Principal Component Analysis; Vertebra

Authors

TYSHCHENKO, B.

Released

25. 4. 2019

Publisher

Brno University of Technology

Location

Brno

ISBN

978-80-214-5735-5

Book

Proceedings of the 25th Conference STUDENT EEICT 2019

Edition number

první

Pages from

390

Pages to

393

Pages count

4

URL

http://www.feec.vutbr.cz/conf/EEICT/archiv/sborniky/EEICT_2019_sbornik.pdf

BibTex

@inproceedings{BUT156826,
  author="Bohdan {Tyshchenko}",
  title="DETECTION OF PATHOLOGICAL VERTEBRAE IN SPINAL CT UTILISED BY MACHINE LEARNING METHODS",
  booktitle="Proceedings of the 25th  Conference STUDENT EEICT 2019",
  year="2019",
  number="první",
  pages="390--393",
  publisher="Brno University of Technology",
  address="Brno",
  isbn="978-80-214-5735-5",
  url="http://www.feec.vutbr.cz/conf/EEICT/archiv/sborniky/EEICT_2019_sbornik.pdf"
}