基于类激活图的可解释迁移学习模型用于fMRI数据的ADHD自动检测。

IF 1.6 4区 医学 Q3 CLINICAL NEUROLOGY Clinical EEG and Neuroscience Pub Date : 2023-03-01 DOI:10.1177/15500594221122699
Caglar Uyulan, Turker Tekin Erguzel, Omer Turk, Shams Farhad, Baris Metin, Nevzat Tarhan
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引用次数: 2

摘要

由于解决了数据的维数诅咒问题,基于功能磁共振成像(fMRI)的深度学习自动检测注意缺陷多动障碍(ADHD)已成为一种非常有用的方法。此外,该方法还提供了一种侵入性和鲁棒性的解决方案,以解决数据采集的差异和类分布的不平衡。本文采用迁移学习方法,即ResNet-50型预训练2d -卷积神经网络(CNN)对ADHD儿童和健康儿童进行自动分类。结果表明,采用10-k交叉验证(CV)的ResNet-50架构,总体分类准确率达到93.45%。对结果的解释是通过班级激活图(CAM)分析完成的,该分析显示多动症儿童在包括额叶、顶叶和颞叶在内的广泛的大脑区域与对照组不同。
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A Class Activation Map-Based Interpretable Transfer Learning Model for Automated Detection of ADHD from fMRI Data.

Automatic detection of Attention Deficit Hyperactivity Disorder (ADHD) based on the functional Magnetic Resonance Imaging (fMRI) through Deep Learning (DL) is becoming a quite useful methodology due to the curse of-dimensionality problem of the data is solved. Also, this method proposes an invasive and robust solution to the variances in data acquisition and class distribution imbalances. In this paper, a transfer learning approach, specifically ResNet-50 type pre-trained 2D-Convolutional Neural Network (CNN) was used to automatically classify ADHD and healthy children. The results demonstrated that ResNet-50 architecture with 10-k cross-validation (CV) achieves an overall classification accuracy of 93.45%. The interpretation of the results was done via the Class Activation Map (CAM) analysis which showed that children with ADHD differed from controls in a wide range of brain areas including frontal, parietal and temporal lobes.

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来源期刊
Clinical EEG and Neuroscience
Clinical EEG and Neuroscience 医学-临床神经学
CiteScore
5.20
自引率
5.00%
发文量
66
审稿时长
>12 weeks
期刊介绍: Clinical EEG and Neuroscience conveys clinically relevant research and development in electroencephalography and neuroscience. Original articles on any aspect of clinical neurophysiology or related work in allied fields are invited for publication.
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