论文标题
基于功能连通性的ADHD分类使用不同的Atlases
Functional Connectivity Based Classification of ADHD Using Different Atlases
论文作者
论文摘要
如今，神经精神疾病的计算诊断策略每天都在受到关注。基于功能 - 磁性图像（fMRI）来确定大脑的功能连通性以诊断疾病至关重要。它被称为慢性疾病，数百万儿童积累了这种疾病的症状，因此研究人员有很多真空制定模型以提高准确诊断ADHD的准确性。在本文中，我们考虑了使用各种时间模板/地图集提取的大脑的功能连接性。局部二进制编码方法（LBEM）算法用于特征提取，而层次 - 极端学习 - 光听（Helm）用于对提取的特征进行分类。为了验证我们的方法，使用143个正常和100个ADHD受影响儿童的fMRI数据用于实验目的。我们的实验结果基于比较给出的各种ATLase，例如CC400，CC200和AAL。与其他地图集相比，我们的模型可以用CC400实现高性能
These days, computational diagnosis strategies of neuropsychiatric disorders are gaining attention day by day. It's critical to determine the brain's functional connectivity based on Functional-Magnetic-Resonance-Imaging(fMRI) to diagnose the disorder. It's known as a chronic disease, and millions of children amass the symptoms of this disease, so there is much vacuum for the researcher to formulate a model to improve the accuracy to diagnose ADHD accurately. In this paper, we consider the functional connectivity of a brain extracted using various time templates/Atlases. Local-Binary Encoding-Method (LBEM) algorithm is utilized for feature extraction, while Hierarchical- Extreme-Learning-Machine (HELM) is used to classify the extracted features. To validate our approach, fMRI data of 143 normal and 100 ADHD affected children is used for experimental purpose. Our experimental results are based on comparing various Atlases given as CC400, CC200, and AAL. Our model achieves high performance with CC400 as compared to other Atlases
