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Entropy 2018, 20(4), 254; https://doi.org/10.3390/e20040254

Multiple Sclerosis Identification Based on Fractional Fourier Entropy and a Modified Jaya Algorithm

1
School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo 454000, China
2
Department of Informatics, University of Leicester, Leicester LE1 7RH, UK
3
Department of Electrical Engineering, the City College of New York, CUNY, New York, NY 10031, USA
4
Department of Neurology, First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China
5
West Virginia School of Osteopathic Medicine, 400 N Lee St, Lewisburg, WV 24901, USA
*
Author to whom correspondence should be addressed.
Received: 30 January 2018 / Revised: 29 March 2018 / Accepted: 3 April 2018 / Published: 5 April 2018
(This article belongs to the Special Issue Entropy-based Data Mining)
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Abstract

Aim: Currently, identifying multiple sclerosis (MS) by human experts may come across the problem of “normal-appearing white matter”, which causes a low sensitivity. Methods: In this study, we presented a computer vision based approached to identify MS in an automatic way. This proposed method first extracted the fractional Fourier entropy map from a specified brain image. Afterwards, it sent the features to a multilayer perceptron trained by a proposed improved parameter-free Jaya algorithm. We used cost-sensitivity learning to handle the imbalanced data problem. Results: The 10 × 10-fold cross validation showed our method yielded a sensitivity of 97.40 ± 0.60%, a specificity of 97.39 ± 0.65%, and an accuracy of 97.39 ± 0.59%. Conclusions: We validated by experiments that the proposed improved Jaya performs better than plain Jaya algorithm and other latest bioinspired algorithms in terms of classification performance and training speed. In addition, our method is superior to four state-of-the-art MS identification approaches. View Full-Text
Keywords: multiple sclerosis; Jaya algorithm; cost-sensitive learning; fractional Fourier entropy; multilayer perceptron; feedforward neural network; k-fold cross validation multiple sclerosis; Jaya algorithm; cost-sensitive learning; fractional Fourier entropy; multilayer perceptron; feedforward neural network; k-fold cross validation
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Wang, S.-H.; Cheng, H.; Phillips, P.; Zhang, Y.-D. Multiple Sclerosis Identification Based on Fractional Fourier Entropy and a Modified Jaya Algorithm. Entropy 2018, 20, 254.

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