Fully Connected Cascade Artificial Neural Network Architecture for Attention Deficit Hyperactivity Disorder Classification From Functional Magnetic Resonance Imaging Data

• Published in: IEEE transactions on cybernetics Vol. 45; no. 12; pp. 2668 - 2679
• Main Authors: Deshpande, Gopikrishna, Peng Wang, Rangaprakash, D., Wilamowski, Bogdan
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.12.2015; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Architecture (computers); Artificial neural networks; Artificial neural networks (ANNs); Attention Deficit Disorder with Hyperactivity - diagnosis; Attention deficit hyperactivity disorder; attention deficit hyperactivity disorder (ADHD); Brain - physiology; Cascades; Classification; Competition; Computer architecture; Disorders; FCC; functional magnetic resonance imaging (fMRI); Humans; Hyperactivity; Image Processing, Computer-Assisted; Learning theory; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Neural networks; Neural Networks (Computer); Neurons; Signal Processing, Computer-Assisted; Support Vector Machine; support vector machines (SVMs); Training; functional magnetic resonance imaging (fMRI); Artificial neural networks (ANNs); classification; attention deficit hyperactivity disorder (ADHD); support vector machines (SVMs)

Automated recognition and classification of brain diseases are of tremendous value to society. Attention deficit hyperactivity disorder (ADHD) is a diverse spectrum disorder whose diagnosis is based on behavior and hence will benefit from classification utilizing objective neuroimaging measures. Tow...