ADD-Net: An Effective Deep Learning Model for Early Detection of Alzheimer Disease in MRI Scans-Reference-Cited by-同舟云学术

ADD-Net: An Effective Deep Learning Model for Early Detection of Alzheimer Disease in MRI Scans

Published:2022 Issue: Volume:10 Page:96930-96951
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Fareed Mian Muhammad Sadiq¹^ORCID,Zikria Shahid²,Ahmed Gulnaz¹,Mui-Zzud-Din ³,Mahmood Saqib⁴,Aslam Muhammad⁵,Jillani Syeda Fizzah⁶,Moustafa Ahmad⁷,Asad Muhammad⁸

Affiliation:

1. Institute of Artificial Intelligence and Marine Robots, Dalian Maritime University, Dalian, China

2. Department of Computer Science, Information Technology University, Lahore, Pakistan

3. Department of Computer Science, Ghazi University, Dera Ghazi Khan, Pakistan

4. Department of Computer Science, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, Pakistan

5. School of Computing Engineering and Physical Sciences, University of the West of Scotland, Glasgow, U.K

6. Department of Physics, Physical Sciences Building, Aberystwyth University, Aberystwyth, U.K

7. Department of Computer Science, Nagoya Institute of Technology, Nagoya, Japan

8. Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan

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/ielx7/6287639/9668973/09877809.pdf?arnumber=9877809

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3. Deep learning type convolution neural network architecture for multiclass classification of Alzheimer’s disease;battineni;Bioimaging,2021

4. Efficient image classification for Alzheimer’s disease prediction using capsule network;vasukidevi;Annals of the Romanian Society for Cell Biology,2021

5. Detection of Alzheimer’s disease (AD) in MRI images using deep learning;pradhan;International Journal of Engineering Research and Technology (IJERT),2021

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