AlzheimerNet: An Effective Deep Learning Based Proposition for Alzheimer’s Disease Stages Classification From Functional Brain Changes in Magnetic Resonance Images-Reference-Cited by-同舟云学术

AlzheimerNet: An Effective Deep Learning Based Proposition for Alzheimer’s Disease Stages Classification From Functional Brain Changes in Magnetic Resonance Images

Published:2023 Issue: Volume:11 Page:16376-16395
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Shamrat F M Javed Mehedi¹^ORCID,Akter Shamima²^ORCID,Azam Sami³^ORCID,Karim Asif³^ORCID,Ghosh Pronab⁴,Tasnim Zarrin¹,Hasib Khan Md.⁵^ORCID,De Boer Friso³^ORCID,Ahmed Kawsar⁶^ORCID

Affiliation:

1. Department of Software Engineering, Daffodil International University, Dhaka, Bangladesh

2. Department of Bioinformatics and Computational Biology, George Mason University, Fairfax, VA, USA

3. Faculty of Science and Technology, Charles Darwin University, Casuarina, NT, Australia

4. Department of Computer Science, Lakehead University, Thunder Bay, ON, Canada

5. Department of Computer Science and Engineering, Ahsanullah University of Science and Technology, Dhaka, Bangladesh

6. Department of Electrical and Computer Engineering, University of Saskatchewan, Saskatoon, SK, Canada

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/ielx7/6287639/10005208/10043854.pdf?arnumber=10043854

Reference60 articles.

1. Deep learning reveals Alzheimer's disease onset in MCI subjects: Results from an international challenge

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3. Multi-Modality Cascaded Convolutional Neural Networks for Alzheimer’s Disease Diagnosis

4. AcneNet - A Deep CNN Based Classification Approach for Acne Classes

5. 3D CNN-based classification using sMRI and MD-DTI images for Alzheimer disease studies;khvostikov;arXiv 1801 05968,2018

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