Lightweight deep residual network for alzheimer’s disease classification using sMRI slices
Author:
Affiliation:
1. College of Electronics and Information Engineering, Sichuan University, Chengdu, Sichuan, P R China
2. Department of Neurology, Chengdu Third People’s Hospital, Chengdu, Sichuan, P R China
Abstract
Publisher
IOS Press
Subject
Artificial Intelligence,General Engineering,Statistics and Probability
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4. The impact of cognitive reserve on brain functional connectivity in Alzheimer’s disease;Bozzali;Journal of Alzheimer’s Disease: JAD,2014
5. Machine learning of neuroimaging for assisted diagnosis of cognitive impairment and dementia: A systematic review. Alzheimer’s & Dementia: Diagnosis;Pellegrini;Assessment & Disease Monitoring,2018
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1. MSFNet‐2SE: A multi‐scale fusion convolutional network for Alzheimer's disease classification on magnetic resonance images;International Journal of Imaging Systems and Technology;2024-05-30
2. Lightweight neural network for Alzheimer's disease classification using multi-slice sMRI;Magnetic Resonance Imaging;2024-04
3. Study on MRI Slices-based Lightweight Neural Network in Alzheimer's Disease Detection;2023 3rd International Conference on Neural Networks, Information and Communication Engineering (NNICE);2023-02-24
4. Reply to Nicholas et al. Using a ResNet-18 Network to Detect Features of Alzheimer’s Disease on Functional Magnetic Resonance Imaging: A Failed Replication. Comment on “Odusami et al. Analysis of Features of Alzheimer’s Disease: Detection of Early Stage from Functional Brain Changes in Magnetic Resonance Images Using a Finetuned ResNet18 Network. Diagnostics 2021, 11, 1071”;Diagnostics;2022-04-27
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