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About A Deep Learning Approach For Automated Diagnosis and Multi-Class Classification of Alzheimer's Disease Stages Using Resting-State fMRI and Residual Neural Networks
This document summarizes a research study that used deep learning and residual neural networks to classify Alzheimer's disease stages using resting-state fMRI data. The study achieved high accuracy in multi-class classification of Alzheimer's disease stages, including cognitively normal (CN), significant memory concern (SMC), early mild cognitive impairment (EMCI), mild cognitive impairment (MCI), late mild cognitive impairment (LMCI), and Alzheimer's disease (AD). Using a dataset of 138 subjects from the Alzheimer's Disease Neuroimaging Initiative, the fine-tuned residual neural network model achieved classification accuracies ranging from 96.85% to 100% for the different disease stages. This suggests deep learning approaches have potential for assisting in early and
Detailed Information
| Author: | ['Carlos Cézar Conrad'] |
|---|---|
| Publication Year: | 2021 |
| Pages: | 16 |
| Language: | English |
| Format: | |
| Price: | FREE |
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