Automated glioma grading based on an efficient ensemble design of a multiple classifier system using deep iteration neural networks matrix

Al-Zurfi, A, Meziane, F ORCID: https://orcid.org/0000-0001-9811-6914 and Aspin, R 2018, Automated glioma grading based on an efficient ensemble design of a multiple classifier system using deep iteration neural networks matrix , in: The 24th International Conference on Automation and Computing (ICAC'18), 6-7 September 2018, Newcastle, UK.

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Abstract

The preoperative diagnosis of brain Glioma grades is crucial for therapeutic planning as it impacts on the tumour’s prognosis. The development of machine learning methods that can accurately evaluate Glioma grades is of great interest since it is a repeatable and reliable diagnosis procedure. Moreover, the classification accuracy of a single classifier can be further improved by using the ensemble of different classifiers. In this paper, a new strategy has been developed, which uses a deep neural network incorporating an extensive iteration matrix based on the combination of eleven different machine learning algorithms. The classification system is evaluated using a cross-validation technique, to add more generalization to the results of the classification system’s reliability in unseen cases. Experimental results indicate that, when compared to both the single classification model, and the majority vote scheme, the grading accuracy has significantly improved using our proposed approach. The obtained sensitivity, specificity and accuracy are 100%, 90% and 93.3% respectively. The proposed approach has improved upon the highest accuracy of the single classification model by 13.3%. The proposed classification system presents an efficient method to evaluate the malignancy level of Glioma with more reliable and accurate clinical outcomes.

Item Type: Conference or Workshop Item (Paper)
Schools: Schools > School of Computing, Science and Engineering > Salford Innovation Research Centre (SIRC)
Depositing User: Prof Farid Meziane
Date Deposited: 16 Jul 2018 09:53
Last Modified: 10 Sep 2018 10:32
URI: http://usir.salford.ac.uk/id/eprint/47701

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