Open AccessClassification of Attention Deficit Hyperactivity Disorder using Variational Autoencoder
Author(s) -
Azurah A. Samah,
Siti Nurul Aqilah Ahmad,
Hairudin Abdul Majid,
Zuraini Ali Shah,
Haslina Hashim,
Nuraina Syaza Azman,
Nur Sabrina Azmi,
Dewi Nasien
Publication year - 2021
Publication title -
international journal of innovative computing
Language(s) - English
Resource type - Journals
ISSN - 2180-4370
DOI - 10.11113/ijic.v11n2.352
Subject(s) - autoencoder , functional magnetic resonance imaging , attention deficit hyperactivity disorder , artificial intelligence , categorization , computer science , pattern recognition (psychology) , deep learning , functional connectivity , region of interest , correlation , psychology , machine learning , neuroscience , psychiatry , mathematics , geometry
Attention Deficit Hyperactivity Disorder (ADHD) categorize as one of the typical neurodevelopmental and mental disorders. Over the years, researchers have identified ADHD as a complicated disorder since it is not directly tested with a standard medical test such as a blood or urine test on the early-stage diagnosis. Apart from the physical symptoms of ADHD, clinical data of ADHD patients show that most of them have learning problems. Therefore, functional Magnetic Resonance Imaging (fMRI) is considered the most suitable method to determine functional activity in the brain region to understand brain disorders of ADHD. One of the ways to diagnose ADHD is by using deep learning techniques, which can increase the accuracy of predicting ADHD using the fMRI dataset. Past attempts of classifying ADHD based on functional connectivity coefficient using the Deep Neural Network (DNN) result in 95% accuracy. As Variational Autoencoder (VAE) is the most popular in extracting high-level data, this model is applied in this study. This study aims to enhance the performance of VAE to increase the accuracy in classifying ADHD using fMRI data based on functional connectivity analysis. The preprocessed fMRI dataset is used for decomposition to find the region of interest (ROI), followed by Independent Component Analysis (ICA) that calculates the correlation between brain regions and creates functional connectivity matrices for each subject. As a result, the VAE model achieved an accuracy of 75% on classifying ADHD.