Attention Deficit Hyperactivity Disorder identification: FMRI data analyzed with CNN and seed-based approach
Attention Deficit Hyperactivity Disorder (ADHD) is a prevalent mental disorder affecting both adults and children, frequently leading to academic difficulties. This study aims to improve the diagnosis of ADHD in children by using resting-state Functional Magnetic Resonance Imaging (fMRI) data. The m...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | Brain Disorders |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666459325000186 |
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| author | Anika Siamin Oyshi Mohammad Hasan Md. Khabir Uddin Ahamed Md. Sydur Rahman Md. Mahfuzul Haque Mahmudul Alam |
| author_facet | Anika Siamin Oyshi Mohammad Hasan Md. Khabir Uddin Ahamed Md. Sydur Rahman Md. Mahfuzul Haque Mahmudul Alam |
| author_sort | Anika Siamin Oyshi |
| collection | DOAJ |
| description | Attention Deficit Hyperactivity Disorder (ADHD) is a prevalent mental disorder affecting both adults and children, frequently leading to academic difficulties. This study aims to improve the diagnosis of ADHD in children by using resting-state Functional Magnetic Resonance Imaging (fMRI) data. The method use seed coherence to identify functional connections between specific seed areas and all brain voxels, focusing on Default Mode Network (DMN) regions pertinent to the diagnosis of ADHD. Convolutional Neural Networks (CNNs) are utilized in classification tasks because of their capacity to learn intricate spatial hierarchies. The research utilizes fMRI scans from the ADHD 200 - Global Competitive dataset, comprising 776 subjects from three prominent data centers. The methodology entails data preparation, feature extraction via seed-based correlation, and classification with Convolutional Neural Networks (CNNs). Three classifiers were assessed: a Neural Network (Keras Sequential Model), a Support Vector Machine (SVM), and a Random Forest Classifier. The optimal outcome was achieved by the neural network, which harmonized precision, recall, and F1 scores, attaining an accuracy of 97 %. The SVM demonstrated considerable accuracy at 83 %, however the Random Forest Classifier exhibited a mere 50 % accuracy, underscoring the necessity for enhancement. These results underscore the merits and shortcomings of each classifier and offer suggestions for enhancement. The paper highlights the significance of Neural Networks for attaining precise and equitable forecasts, proposes enhancements for the Support Vector Machine, and stresses the imperative of optimizing the Random Forest Classifier. This study enhances ADHD diagnosis by methodically employing neuroimaging techniques and assessing several classifiers, leading to a reliable diagnostic system. |
| format | Article |
| id | doaj-art-aa8151c1edcd4861903980086f5077de |
| institution | Kabale University |
| issn | 2666-4593 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Brain Disorders |
| spelling | doaj-art-aa8151c1edcd4861903980086f5077de2025-08-20T03:42:37ZengElsevierBrain Disorders2666-45932025-03-011710019810.1016/j.dscb.2025.100198Attention Deficit Hyperactivity Disorder identification: FMRI data analyzed with CNN and seed-based approachAnika Siamin Oyshi0Mohammad Hasan1Md. Khabir Uddin Ahamed2Md. Sydur Rahman3Md. Mahfuzul Haque4Mahmudul Alam5Department of Computer Science and Engineering, Jamalpur Science and Technology University, Jamalpur-2012, BangladeshDepartment of Computer Science and Engineering, Jamalpur Science and Technology University, Jamalpur-2012, BangladeshDepartment of Computer Science and Engineering, Jamalpur Science and Technology University, Jamalpur-2012, BangladeshDepartment of Computer Science and Engineering, Jamalpur Science and Technology University, Jamalpur-2012, BangladeshDepartment of Electrical and Electronic Engineering, Jamalpur Science and Technology University, Jamalpur-2012, BangladeshDepartment of Computer Science and Engineering, Jamalpur Science and Technology University, Jamalpur-2012, Bangladesh; Corresponding author.Attention Deficit Hyperactivity Disorder (ADHD) is a prevalent mental disorder affecting both adults and children, frequently leading to academic difficulties. This study aims to improve the diagnosis of ADHD in children by using resting-state Functional Magnetic Resonance Imaging (fMRI) data. The method use seed coherence to identify functional connections between specific seed areas and all brain voxels, focusing on Default Mode Network (DMN) regions pertinent to the diagnosis of ADHD. Convolutional Neural Networks (CNNs) are utilized in classification tasks because of their capacity to learn intricate spatial hierarchies. The research utilizes fMRI scans from the ADHD 200 - Global Competitive dataset, comprising 776 subjects from three prominent data centers. The methodology entails data preparation, feature extraction via seed-based correlation, and classification with Convolutional Neural Networks (CNNs). Three classifiers were assessed: a Neural Network (Keras Sequential Model), a Support Vector Machine (SVM), and a Random Forest Classifier. The optimal outcome was achieved by the neural network, which harmonized precision, recall, and F1 scores, attaining an accuracy of 97 %. The SVM demonstrated considerable accuracy at 83 %, however the Random Forest Classifier exhibited a mere 50 % accuracy, underscoring the necessity for enhancement. These results underscore the merits and shortcomings of each classifier and offer suggestions for enhancement. The paper highlights the significance of Neural Networks for attaining precise and equitable forecasts, proposes enhancements for the Support Vector Machine, and stresses the imperative of optimizing the Random Forest Classifier. This study enhances ADHD diagnosis by methodically employing neuroimaging techniques and assessing several classifiers, leading to a reliable diagnostic system.http://www.sciencedirect.com/science/article/pii/S2666459325000186Attention Deficit Hyperactivity Disorder (ADHD)Functional Magnetic Resonance Imaging (fMRI)Default Mode Network (DMN)Seed-based correlation techniqueConvolutional Neural Network (CNN)Independent Component Analysis (ICA) |
| spellingShingle | Anika Siamin Oyshi Mohammad Hasan Md. Khabir Uddin Ahamed Md. Sydur Rahman Md. Mahfuzul Haque Mahmudul Alam Attention Deficit Hyperactivity Disorder identification: FMRI data analyzed with CNN and seed-based approach Brain Disorders Attention Deficit Hyperactivity Disorder (ADHD) Functional Magnetic Resonance Imaging (fMRI) Default Mode Network (DMN) Seed-based correlation technique Convolutional Neural Network (CNN) Independent Component Analysis (ICA) |
| title | Attention Deficit Hyperactivity Disorder identification: FMRI data analyzed with CNN and seed-based approach |
| title_full | Attention Deficit Hyperactivity Disorder identification: FMRI data analyzed with CNN and seed-based approach |
| title_fullStr | Attention Deficit Hyperactivity Disorder identification: FMRI data analyzed with CNN and seed-based approach |
| title_full_unstemmed | Attention Deficit Hyperactivity Disorder identification: FMRI data analyzed with CNN and seed-based approach |
| title_short | Attention Deficit Hyperactivity Disorder identification: FMRI data analyzed with CNN and seed-based approach |
| title_sort | attention deficit hyperactivity disorder identification fmri data analyzed with cnn and seed based approach |
| topic | Attention Deficit Hyperactivity Disorder (ADHD) Functional Magnetic Resonance Imaging (fMRI) Default Mode Network (DMN) Seed-based correlation technique Convolutional Neural Network (CNN) Independent Component Analysis (ICA) |
| url | http://www.sciencedirect.com/science/article/pii/S2666459325000186 |
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