research-article

Comparison of machine learning methods for stationary wavelet entropy-based multiple sclerosis detection

Authors:

Preetha Phillips,

Shuihua WangAuthors Info & Claims

Simulation, Volume 92, Issue 9

Pages 861 - 871

https://doi.org/10.1177/0037549716666962

Published: 01 September 2016 Publication History

Abstract

In order to detect multiple sclerosis MS subjects from healthy controls HCs in magnetic resonance imaging, we developed a new system based on machine learning. The MS imaging data was downloaded from the eHealth laboratory at the University of Cyprus, and the HC imaging data was scanned in our local hospital with volunteers enrolled from community advertisement. Inter-scan normalization was employed to remove the gray-level difference. We adjust the misclassification costs to alleviate the effect of unbalanced class distribution to the classification performance. We utilized two-level stationary wavelet entropy SWE to extract features from brain images. Then, we compared three machine learning based classifiers: the decision tree, k-nearest neighbors kNN, and support vector machine. The experimental results showed the kNN performed the best among all three classifiers. In addition, the proposed SWE+kNN approach is superior to four state-of-the-art approaches. Our proposed MS detection approach is effective.

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Comparison of machine learning methods for stationary wavelet entropy-based multiple sclerosis detection
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cover image Simulation

Simulation Volume 92, Issue 9

9 2016

58 pages

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Copyright © © The Authors 2016.

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Society for Computer Simulation International

San Diego, CA, United States

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Published: 01 September 2016

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Saini MSusan S(2023)Tackling class imbalance in computer vision: a contemporary reviewArtificial Intelligence Review10.1007/s10462-023-10557-656:Suppl 1(1279-1335)Online publication date: 20-Jul-2023
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Sah MDirekoglu C(2022)A survey of deep learning methods for multiple sclerosis identification using brain MRI imagesNeural Computing and Applications10.1007/s00521-022-07099-334:10(7349-7373)Online publication date: 1-May-2022
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