ADHD Identification using Convolutional Neural Network with Seed-based Approach for fMRI Data
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Attention Deficit Hyperactivity Disorder (ADHD) is a highly prevalent psychiatric disorder with persistent patterns of inattention, hyperactivity and impulsivity behaviors among children. The perilous factor lies underneath is that often these children are commonly entangled with learning difficulties which tend to lead frustration when they reach adulthood. This study presents an effective approach for ADHD identification at an early stage by using functional Magnetic Resonance Imaging data for the resting-state brain. The proposed methodology is based on seed correlation which computes the functional connectivity between seeds and all other voxels within the brain. The classification is done using Convolution Neural Network based on extracted seed correlations from different Default Mode Network (DMN) regions. The proposed method using correlation on DMN regions has shown significant accuracies between 84% and 86% to be used with CNN for the identification of ADHD.
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