A computer-aided diagnosis system for glioma grading using three dimensional texture analysis and machine learning in mri brain tumour

Conference paper

Al-Zurfi, A.N., Meziane, F. and Aspin, R. 2019. A computer-aided diagnosis system for glioma grading using three dimensional texture analysis and machine learning in mri brain tumour. 2019 3rd International Conference on Bio-engineering for Smart Technologies (BioSMART). Paris, France 24 - 26 Apr 2019 IEEE Xplore. https://doi.org/10.1109/biosmart.2019.8734207
AuthorsAl-Zurfi, A.N., Meziane, F. and Aspin, R.
TypeConference paper
Abstract

Glioma grading is vital for therapeutic planning where the higher level of glioma is associated with high mortality. It is a challenging task as different glioma grades have mixed morphological characteristics of brain tumour. A computeraided diagnosis (CAD) system based on three-dimensional textural grey level co-occurrence matrix (GLCM) and machine learning is proposed for glioma grading. The purpose of this paper is to assess the usefulness of the 3D textural analysis in establishing a malignancy prediction model for glioma grades. Furthermore, this paper aims to find the best classification model based on textural analysis for glioma grading. The classification system was evaluated using leave-one-out cross-validation technique. The experimental design includes feature extraction, feature selection, and finally the classification that includes single and ensemble classification models in a comparative study. Experimental results illustrate that single and ensemble classification models, can achieve efficient prediction performance based on 3D textural analysis and the classification accuracy result has significantly improved after using feature selection methods. In this paper, we compare the proficiency of applying different angles of 3D textural analysis and different classification models to determine the malignant level of glioma. The obtained sensitivity, accuracy and specificity are 100%, 96.6%, 90% respectively. The prediction system presents an effective approach to assess the malignancy level of glioma with a non-invasive, reproducible and accurate CAD system for glioma grading.

KeywordsGlioma Grading; Grey level co-occurrence matrix; Machine Learning algorithm; Single and ensemble classification system
Year2019
Conference2019 3rd International Conference on Bio-engineering for Smart Technologies (BioSMART)
JournalBioSMART 2019 - Proceedings: 3rd International Conference on Bio-Engineering for Smart Technologies
PublisherIEEE Xplore
Digital Object Identifier (DOI)https://doi.org/10.1109/biosmart.2019.8734207
Web address (URL)http://www.scopus.com/inward/record.url?eid=2-s2.0-85068349004&partnerID=MN8TOARS
Journal citation(Article: 8734207)
ISBN978-1-7281-3578-6
Web address (URL) of conference proceedingshttps://ieeexplore.ieee.org/xpl/conhome/8732298/proceeding
Output statusPublished
Publication dates
Online13 Jun 2019
Publication process dates
Deposited05 Jun 2023
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