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Complexity
Volume 2018, Article ID 3198184, 13 pages
https://doi.org/10.1155/2018/3198184
Research Article

Identification of Alcoholism Based on Wavelet Renyi Entropy and Three-Segment Encoded Jaya Algorithm

1School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, Henan 454000, China
2Department of Informatics, University of Leicester, Leicester LE1 7RH, UK
3School of Computer Science and Technology, Nanjing Normal University, Nanjing, Jiangsu 210023, China
4Digital Contents Research Institute, Sejong University, Seoul, Republic of Korea
5Department of Psychiatry, Nanjing Brain Hospital, Nanjing Medical University, Nanjing, Jiangsu 210029, China
6School of Computing, Mathematics and Digital Technology (SCMDT), Manchester Metropolitan University, Manchester M156BH, UK

Correspondence should be addressed to Khan Muhammad; rk.ca.ujs@dammahumnahk, Yuxiu Sui; moc.nuyila@uixuyius, Liangxiu Han; ku.ca.umm@nah.l, and Yu-Dong Zhang; gro.eeei@gnahzgnoduy

Received 11 September 2017; Revised 22 November 2017; Accepted 12 December 2017; Published 17 January 2018

Academic Editor: Javier Escudero

Copyright © 2018 Shui-Hua Wang et al. 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.

Abstract

The alcohol use disorder (AUD) is an important brain disease, which could cause the damage and alteration of brain structure. The current diagnosis of AUD is mainly done manually by radiologists. This study proposes a novel computer-vision-based method for automatic detection of AUD based on wavelet Renyi entropy and three-segment encoded Jaya algorithm from MRI scans. The wavelet Renyi entropy is proposed to provide multiresolution and multiscale analysis of features, describe the complexity of the brain structure, and extract the distinctive features. Grid search method was used to select the optimal wavelet decomposition level and Renyi order. The classifier was constructed based on feedforward neural network and a three-segment encoded (TSE) Jaya algorithm providing parameter-free training of the weights, biases, and number of hidden neurons. We have conducted the experimental evaluation on 235 subjects (114 are AUDs and 121 healthy). -fold cross validation has been used to avoid overfitting and report out-of-sample errors. The results showed that the proposed method outperforms four state-of-the-art approaches in terms of accuracy. The proposed TSE-Jaya provides a better performance, compared to the conventional approaches including plain Jaya, multiobjective genetic algorithm, particle swarm optimization, bee colony optimization, modified ant colony system, and real-coded biogeography-based optimization.