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A Review of Computational Intelligence Models for Brain Tumour Classification and Prediction

A Review of Computational Intelligence Models for Brain Tumour Classification and Prediction

Justice Kwame Appati, Godfred Akwetey Brown, Michael Agbo Tettey Soli, Ismail Wafaa Denwar
Copyright: © 2021 |Volume: 13 |Issue: 4 |Pages: 22
ISSN: 1942-9045|EISSN: 1942-9037|EISBN13: 9781799860679|DOI: 10.4018/IJSSCI.2021100102
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MLA

Appati, Justice Kwame, et al. "A Review of Computational Intelligence Models for Brain Tumour Classification and Prediction." IJSSCI vol.13, no.4 2021: pp.18-39. http://doi.org/10.4018/IJSSCI.2021100102

APA

Appati, J. K., Brown, G. A., Soli, M. A., & Denwar, I. W. (2021). A Review of Computational Intelligence Models for Brain Tumour Classification and Prediction. International Journal of Software Science and Computational Intelligence (IJSSCI), 13(4), 18-39. http://doi.org/10.4018/IJSSCI.2021100102

Chicago

Appati, Justice Kwame, et al. "A Review of Computational Intelligence Models for Brain Tumour Classification and Prediction," International Journal of Software Science and Computational Intelligence (IJSSCI) 13, no.4: 18-39. http://doi.org/10.4018/IJSSCI.2021100102

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Abstract

This review aims to systematically analyze ML models from four aspects: type of ML technique, estimation accuracy, model comparison, and estimation context. A systematic literature review of empirical studies was conducted on the ML models published in the last decades. Fifty-one primary studies relevant to the objective of this research were revealed. After investigating these studies, five ML techniques have been employed in brain tumor classification and prediction. Ultimately, the estimation accuracy of these ML models could be regarded and accepted and outperformed non-ML models. ML models have been revealed to be useful in brain tumor classification and prediction. Genetic algorithm among the ML models achieved an accuracy of 100%. Nevertheless, ML models are still restricted in the industry, so initiative and encouragement are needed to make ML models easier. Further work is required on these ML models to verify the accuracy and consider other performance metrics other than the accuracy.

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