Detail publikace

The use of machine learning for non-invasive classification of brain pathologies

KOSTIAL, M. MARCOŇ, P. SOLÁR, P.

Originální název

The use of machine learning for non-invasive classification of brain pathologies

Typ

článek ve sborníku mimo WoS a Scopus

Jazyk

angličtina

Originální abstrakt

Accurate classification and spatial delineation of brain pathologies is crucial for correct diagnosis and effective treatment. Currently, CT (computed tomography) and MRI (magnetic resonance imaging) are used for this purpose, and when suspected, a biopsy is performed. The aim of this study is to demonstrate the potential of using machine learning to identify areas of a given pathology based on the diffusivity of individual tissues. KNN (knearest neighbours) and SVM (support vector machine) models were learned on a dataset containing data from patients with glioblastoma Multiforme and Abscessus Cerebri, and then their performance was investigated. The obtained results indicate the high accuracy of the models, which only supports their possibilities and potential for future use in automated diagnostic tools that will reduce the use of biopsy and speed up the whole process.

Klíčová slova

brain tumour, pathology, artificial intelligence, machine learning

Autoři

KOSTIAL, M.; MARCOŇ, P.; SOLÁR, P.

Vydáno

25. 4. 2023

Místo

Brno

Strany od

1

Strany do

5

Strany počet

5

URL

https://www.eeict.cz/download

BibTex

@inproceedings{BUT183989,
  author="Martin {Kostial} and Petr {Marcoň} and Peter {Solár}",
  title="The use of machine learning for non-invasive classification of brain pathologies",
  year="2023",
  pages="1--5",
  address="Brno",
  url="https://www.eeict.cz/download"
}