Classification of brain tumours using radiomic features on MRI

Main Article Content

Gokalp Cinarer
https://orcid.org/0000-0003-0818-6746
Bulent Gursel Emiroglu
https://orcid.org/0000-0002-1656-6450

Abstract

Glioma is one of the most common brain tumours among the diagnoses of existing brain tumours. Glioma grades are important factors that should be known in the treatment of brain tumours. In this study, the radiomic features of gliomas were analysed and glioma grades were classified by Gaussian Naive Bayes algorithm. Glioma tumours of 121 patients of Grade II and Grade III were examined. The glioma tumours were segmented with the Grow Cut Algorithm and the 3D feature of tumour magnetic resonance imaging images were obtained with the 3D Slicer programme. The obtained quantitative values were statistically analysed with Spearman and Mann–Whitney U tests and 21 features with statistically significant properties were selected from 107 features. The results showed that the best performing among the algorithms was Gaussian Naive Bayes algorithm with 80% accuracy. Machine learning and feature selection techniques can be used in the analysis of gliomas as well as pathological evaluations in glioma grading processes.


 


Keywords: Radiomics, glioma, naive bayes.

Downloads

Download data is not yet available.

Article Details

How to Cite
Cinarer, G., & Emiroglu, B. (2020). Classification of brain tumours using radiomic features on MRI. New Trends and Issues Proceedings on Advances in Pure and Applied Sciences, (12), 80-90. https://doi.org/10.18844/gjpaas.v0i12.4989
Section
Articles