Dual Attention Convolutional AutoEncoder for Diagnosis of Alzheimer’s Disorder in Patients Using Neuroimaging and MRI Features-Reference-Cited by-同舟云学术

Dual Attention Convolutional AutoEncoder for Diagnosis of Alzheimer’s Disorder in Patients Using Neuroimaging and MRI Features

Published:2024 Issue: Volume:12 Page:58722-58739
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Al-Otaibi Shaha¹,Mujahid Muhammad²^ORCID,Khan Amjad Rehman²^ORCID,Nobanee Haitham³^ORCID,Alyami Jaber⁴^ORCID,Saba Tanzila²^ORCID

Affiliation:

1. Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P. O. Box 84428, Riyadh, Saudi Arabia

2. Artificial Intelligence and Data Analytics (AIDA) Laboratory, CCIS, Prince Sultan University, Riyadh, Saudi Arabia

3. College of Business, Abu Dhabi University, Abu Dhabi, United Arab Emirates

4. Radiological Sciences Department, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah, Saudi Arabia

Funder

Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia, through the Researchers Supporting Project

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6287639/10380310/10504280.pdf?arnumber=10504280

Reference34 articles.

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5. Early diagnosis of Alzheimer’s disease: the role of biomarkers including advanced EEG signal analysis. Report from the IFCN-sponsored panel of experts