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; Aspin, R

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.

Citation

Al-Zurfi, A., Meziane, F., & Aspin, R. (2018, September). Automated glioma grading based on an efficient ensemble design of a multiple classifier system using deep iteration neural networks matrix. Presented at The 24th International Conference on Automation and Computing (ICAC'18), Newcastle, UK

• Presentation Conference Type: Other
• Conference Name: The 24th International Conference on Automation and Computing (ICAC'18)
• Conference Location: Newcastle, UK
• Start Date: Sep 6, 2018
• End Date: Sep 7, 2018
• Acceptance Date: Jun 6, 2018
• Deposit Date: Jul 16, 2018
• Publicly Available Date: Sep 8, 2018
• Publisher URL: http://www.cacsuk.co.uk/index.php/conferences
• Additional Information: Event Type : Conference