基于早期学习技术的fMRI分析计算方法对自闭症谱系障碍的早期预测

K. P., Yasir Babiker Hamdan, Sathish
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引用次数: 39

摘要

神经成像发育分类研究是用少量脑活动样本进行的。它承诺在高维数据分析的鼓舞人心的复杂性。自闭症的预测方法以前仅基于行为功能,这提供了良好的精度,但收回将是不幸的。我们用神经发育的现代技术解决了这些问题,并与以前的技术进行了比较。此外,大脑活动的可视化在神经成像中非常重要。我们相信在早期月捕获和附加的马伦早期学习量表(MSEL)的神经图像更好的可视化和分类。功能磁共振成像(fMRI)是一种无创测量脑活动的控制工具,具有良好的分辨率。对于脑活动的高分辨率,功能磁共振成像(fMRI)优于脑电图(EEG)。大脑活动的可视化是识别自闭症缺陷的第一步。我们利用机器学习算法的多种行为活动和发展措施来预测早期自闭症谱系障碍(ASD)。目前对预测方法的研究主要集中在对具有超高危险因素的神经影像学进行预测。从多个时间点进行14个月的发展测量,预测ASD的准确性中等。在这项工作中,马伦早期预测被附加到早期预测中,并通过计算方法对机器学习算法的自适应功能分类器进行fMRI分析。该算法为机器语言分类提供了改进版本,具有MSEL和保守方法的高准确率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Early Prediction of Autism Spectrum Disorder by Computational Approaches to fMRI Analysis with Early Learning Technique
The neuro imaging developmental classification studies are undergone with small amount of samples from the brain activity samples. It promises the inspiring complications in high dimensional data analysis. Autism prediction methodologies are based on behavioral function alone previously which provides good precision but repossession will be unfortunate. We address those problems for early prediction of autism with neural development modern techniques and compared with older. Moreover, visualization of brain activities is quite important in neuro imaging. We believe in better visualization and classification of neuro images in early month captures and appended of Mullen Scales of Early Learning (MSEL). Functional magnetic resonance imaging (fMRI) is one of the controlling tools for measuring non-invasively measure brain activity and it provides with good resolution. For high resolution of brain activity, fMRI gives better than electro encephalon graph (EEG). Visualization of brain activity very clearly is first step to recognize the faults of autism. We have taken into the account for predicting in early Autism Spectrum Disorder (ASD) with help of multiple behavioral activities and development measures using machine learning algorithm. The prediction methods are examined with mostly many prediction methods start to examine the neuro imaging with ultra-high risk factors. The prediction of ASD is moderate accuracy in 14 month development measures from multiple time points. In this proposed work, Mullen early prediction is appended for early prediction and it is examined with computational approach to fMRI analysis with adaptive functioning classifier for machine learning algorithm. This proposed algorithm provides improved version of classification in machine languages with MSEL and high accuracy with conservative methods.
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