Uyulan Caglar, Erguzel Turker Tekin, Turk Omer, Farhad Shams, Metin Baris, Tarhan Nevzat
Department of Mechanical Engineering, Faculty of Engineering and Architecture, İzmir Katip Çelebi University, İzmir, Turkey.
Department of Computer Engineering, 232990Uskudar University, Istanbul, Turkey.
Clin EEG Neurosci. 2023 Mar;54(2):151-159. doi: 10.1177/15500594221122699. Epub 2022 Sep 1.
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.
基于深度学习（DL）通过功能磁共振成像（fMRI）自动检测注意力缺陷多动障碍（ADHD），由于解决了数据的高维诅咒问题，正成为一种非常有用的方法。此外，该方法针对数据采集的差异和类分布不平衡提出了一种侵入性且稳健的解决方案。在本文中，采用了一种迁移学习方法，具体为预训练的二维卷积神经网络（CNN）ResNet - 50类型，用于自动对ADHD儿童和健康儿童进行分类。结果表明，采用10折交叉验证（CV）的ResNet - 50架构实现了93.45%的总体分类准确率。通过类激活映射（CAM）分析对结果进行了解释，该分析表明ADHD儿童在包括额叶、顶叶和颞叶在内的广泛脑区与对照组存在差异。
