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

Al-Zurfi, AN, Meziane, F ORCID: https://orcid.org/0000-0001-9811-6914 and Aspin, R ORCID: https://orcid.org/0000-0002-2202-1326 2019, A computer-aided diagnosis system for glioma grading using three dimensional texture analysis and machine learning in MRI brain tumour , in: 3rd IEEE International Conference on Bio-Engineering for Smart Technologies (BioSMART 2019), 24-26 April 2019, Paris, France.

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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 computer-aided 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.

Item Type: Conference or Workshop Item (Paper)
Schools: Schools > School of Computing, Science and Engineering > Salford Innovation Research Centre (SIRC)
Journal or Publication Title: 3rd IEEE International Conference on Bio-Engineering for Smart Technologies (BioSMART 2019)
Publisher: IEEE
Depositing User: Prof Farid Meziane
Date Deposited: 13 May 2019 10:39
Last Modified: 20 May 2019 10:45
URI: http://usir.salford.ac.uk/id/eprint/51291

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