Finding Essential Parts of the Brain in rs-fMRI Can Improve ADHD Diagnosis Using Deep Learning
Author:
Affiliation:
1. Department of Applied Artificial Intelligence, Hanyang University, ERICA Campus, Ansan, Republic of Korea
Funder
Basic Science Research Program through the National Research Foundation of Korea
Ministry of Education, Science and Technology
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/6287639/10005208/10286029.pdf?arnumber=10286029
Reference52 articles.
1. Deep fMRI: AN end-to-end deep network for classification of fMRI data
2. FCNet: A Convolutional Neural Network for Calculating Functional Connectivity from Functional MRI
3. 3D CNN Based Automatic Diagnosis of Attention Deficit Hyperactivity Disorder Using Functional and Structural MRI
4. Separated Channel Attention Convolutional Neural Network (SC-CNN-Attention) to Identify ADHD in Multi-Site Rs-fMRI Dataset
5. Structural brain imaging correlates of ASD and ADHD across the lifespan: a hypothesis-generating review on developmental ASD–ADHD subtypes
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1. A short report on ADHD detection using convolutional neural networks;Frontiers in Psychiatry;2024-09-05
2. Assistive tools for classifying neurological disorders using fMRI and deep learning: A guide and example;Brain and Behavior;2024-06
3. An Improved Model for The Diagnosis of Attention Deficit/Hyperactivity Disorder (ADHD) Using Resting-state fMRI Data;2024 International Conference on Machine Intelligence and Smart Innovation (ICMISI);2024-05-12
4. Revolutionising ADHD diagnosis: deep learning in 3D medical imaging;Fourth International Conference on Signal Processing and Machine Learning (CONF-SPML 2024);2024-04-01
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