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KOSTIAL, M. MARCOŇ, P. SOLÁR, P.
Original Title
The use of machine learning for non-invasive classification of brain pathologies
Type
article in a collection out of WoS and Scopus
Language
English
Original Abstract
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.
Keywords
brain tumour, pathology, artificial intelligence, machine learning
Authors
KOSTIAL, M.; MARCOŇ, P.; SOLÁR, P.
Released
25. 4. 2023
Location
Brno
Pages from
1
Pages to
5
Pages count
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" }