学习迁移
人工智能
卷积神经网络
功能磁共振成像
模式识别(心理学)
计算机科学
注意缺陷多动障碍
班级(哲学)
深度学习
维数之咒
机器学习
心理学
神经科学
精神科
作者
Çağlar Uyulan,Türker Tekin Ergüzel,Ömer Türk,Shams Farhad,Barış Metin,Nevzat Tarhan
标识
DOI:10.1177/15500594221122699
摘要
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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