Classification of attention deficit hyperactivity disorder using variational autoencoder
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...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Penerbit UTM Press
2021
|
| Subjects: | |
| Online Access: | http://eprints.utm.my/97795/1/SitiNurulAqilah2021_ClassificationofAttentionDeficitHyperactivity.pdf |
| _version_ | 1796866493182377984 |
|---|---|
| author | A. Samah, Azurah Ahmad, Siti Nurul Aqilah Abdul Majid, Hairudin Ali Shah, Zuraini Hashim, Haslina Azman, Nuraina Syaza Azmi, Nur Sabrina Dewi Nasien, Dewi Nasien |
| author_facet | A. Samah, Azurah Ahmad, Siti Nurul Aqilah Abdul Majid, Hairudin Ali Shah, Zuraini Hashim, Haslina Azman, Nuraina Syaza Azmi, Nur Sabrina Dewi Nasien, Dewi Nasien |
| author_sort | A. Samah, Azurah |
| collection | ePrints |
| description | 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. |
| first_indexed | 2024-03-05T21:12:58Z |
| format | Article |
| id | utm.eprints-97795 |
| institution | Universiti Teknologi Malaysia - ePrints |
| language | English |
| last_indexed | 2024-03-05T21:12:58Z |
| publishDate | 2021 |
| publisher | Penerbit UTM Press |
| record_format | dspace |
| spelling | utm.eprints-977952022-10-31T08:55:14Z http://eprints.utm.my/97795/ Classification of attention deficit hyperactivity disorder using variational autoencoder A. Samah, Azurah Ahmad, Siti Nurul Aqilah Abdul Majid, Hairudin Ali Shah, Zuraini Hashim, Haslina Azman, Nuraina Syaza Azmi, Nur Sabrina Dewi Nasien, Dewi Nasien QA75 Electronic computers. Computer science 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. Penerbit UTM Press 2021-12 Article PeerReviewed application/pdf en http://eprints.utm.my/97795/1/SitiNurulAqilah2021_ClassificationofAttentionDeficitHyperactivity.pdf A. Samah, Azurah and Ahmad, Siti Nurul Aqilah and Abdul Majid, Hairudin and Ali Shah, Zuraini and Hashim, Haslina and Azman, Nuraina Syaza and Azmi, Nur Sabrina and Dewi Nasien, Dewi Nasien (2021) Classification of attention deficit hyperactivity disorder using variational autoencoder. International Journal of Innovative Computing, 11 (2). pp. 81-87. ISSN 2180-4370 http://dx.doi.org/10.11113/ijic.v11n2.352 DOI:10.11113/ijic.v11n2.352 |
| spellingShingle | QA75 Electronic computers. Computer science A. Samah, Azurah Ahmad, Siti Nurul Aqilah Abdul Majid, Hairudin Ali Shah, Zuraini Hashim, Haslina Azman, Nuraina Syaza Azmi, Nur Sabrina Dewi Nasien, Dewi Nasien Classification of attention deficit hyperactivity disorder using variational autoencoder |
| title | Classification of attention deficit hyperactivity disorder using variational autoencoder |
| title_full | Classification of attention deficit hyperactivity disorder using variational autoencoder |
| title_fullStr | Classification of attention deficit hyperactivity disorder using variational autoencoder |
| title_full_unstemmed | Classification of attention deficit hyperactivity disorder using variational autoencoder |
| title_short | Classification of attention deficit hyperactivity disorder using variational autoencoder |
| title_sort | classification of attention deficit hyperactivity disorder using variational autoencoder |
| topic | QA75 Electronic computers. Computer science |
| url | http://eprints.utm.my/97795/1/SitiNurulAqilah2021_ClassificationofAttentionDeficitHyperactivity.pdf |
| work_keys_str_mv | AT asamahazurah classificationofattentiondeficithyperactivitydisorderusingvariationalautoencoder AT ahmadsitinurulaqilah classificationofattentiondeficithyperactivitydisorderusingvariationalautoencoder AT abdulmajidhairudin classificationofattentiondeficithyperactivitydisorderusingvariationalautoencoder AT alishahzuraini classificationofattentiondeficithyperactivitydisorderusingvariationalautoencoder AT hashimhaslina classificationofattentiondeficithyperactivitydisorderusingvariationalautoencoder AT azmannurainasyaza classificationofattentiondeficithyperactivitydisorderusingvariationalautoencoder AT azminursabrina classificationofattentiondeficithyperactivitydisorderusingvariationalautoencoder AT dewinasiendewinasien classificationofattentiondeficithyperactivitydisorderusingvariationalautoencoder |