An Efficient Attention Deficit Hyperactivity Disorder (ADHD) Diagnostic Technique Based on Multi-Regional Brain Magnetic Resonance Imaging

Q3 Engineering
Sachnev Vasily, B. S. Mahanand
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引用次数: 0

Abstract

In this paper, an efficient technique for the diagnosis of attention deficit hyperactivity disorder (ADHD) was proposed. The proposed method used features/voxels extracted from structural magnetic resonance imaging (MRI) scans of seven brain regions and efficiently classified three subtypes of ADHD: ADHD-C, ADHD-H, and ADHD-I, as well as the typically developing control (TDC). Training and testing data for experiments were obtained from ADHD-200 database, and 41,721 features/voxels were extracted from sMRI by using region-of-interest (ROI). The proposed ADHD diagnostic technique built an efficient ADHD classifier in two steps. In the first step, a proposed regional voxels selection method (rVSM) selected an optimal set of features/voxels from seven brain regions available in ADHD-200, i.e., the Amygdala, Caudate, Cerebellar Vermis, Corpus Callosum, Hippocampus, Striatum, and Thalamus. In the second step, voxels/features selected by rVSM were used together to form a unified set of voxels. The unified set of voxels was used by a multi-region voxels selection method to train an efficient classifier using the extreme learning machine (ELM). Finally, the proposed method selected a unique set of voxels from the seven brain regions and built a final ELM classifier with maximum accuracy. Experiments clearly indicated that the proposed method produced better results than existing methods.
基于多区域脑磁共振成像的注意力缺陷多动障碍(ADHD)高效诊断技术
本文提出了一种诊断注意缺陷多动障碍(ADHD)的有效方法。该方法使用从七个大脑区域的结构磁共振成像(MRI)扫描中提取的特征/体素,有效地分类了ADHD的三种亚型:ADHD- c、ADHD- h和ADHD- i,以及典型发展对照(TDC)。从ADHD-200数据库中获取实验训练和测试数据,利用感兴趣区域(ROI)从sMRI中提取41,721个特征/体素。所提出的ADHD诊断技术分两步建立了一个高效的ADHD分类器。在第一步中,提出的区域体素选择方法(rVSM)从ADHD-200可用的七个大脑区域(即杏仁核、尾状体、小脑蚓、胼胝体、海马、纹状体和丘脑)中选择一组最优的特征/体素。第二步,将rVSM选择的体素/特征一起使用,形成统一的体素集合。利用统一的体素集,采用多区域体素选择方法,利用极限学习机训练出高效的分类器。最后,该方法从7个大脑区域中选择一组独特的体素,构建出具有最大准确率的最终ELM分类器。实验结果表明,本文提出的方法比现有的方法效果更好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Computing Science and Engineering
Journal of Computing Science and Engineering Engineering-Engineering (all)
CiteScore
1.00
自引率
0.00%
发文量
11
期刊介绍: Journal of Computing Science and Engineering (JCSE) is a peer-reviewed quarterly journal that publishes high-quality papers on all aspects of computing science and engineering. The primary objective of JCSE is to be an authoritative international forum for delivering both theoretical and innovative applied researches in the field. JCSE publishes original research contributions, surveys, and experimental studies with scientific advances. The scope of JCSE covers all topics related to computing science and engineering, with a special emphasis on the following areas: Embedded Computing, Ubiquitous Computing, Convergence Computing, Green Computing, Smart and Intelligent Computing, Human Computing.
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