Name
Abstract | ||
|---|---|---|
In the current decade, advances in health care are attracting widespread interest due to their contributions to people longer surviving and fitter lives. Alzheimer's disease (AD) is the commonest neurodegenerative and dementing disease. The monetary value of caring for Alzheimer's disease patients is involved to rise dramatically. The necessity of having a computer-aided system for early and accurate AD classification becomes crucial. Deep-learning algorithms have notable advantages rather than machine learning methods. Many recent research studies that have used brain MRI scans and convolutional neural networks (CNN) achieved promising results for the diagnosis of Alzheimer's disease. Accordingly, this study proposes a CNN based end-to-end framework for AD-classification. The proposed framework achieved 99.6%, 99.8%, and 97.8% classification accuracies on Alzheimer's disease Neuroimaging Initiative (ADNI) dataset for the binary classification of AD and Cognitively Normal (CN). In multi-classification experiments, the proposed framework achieved 97.5% classification accuracy on the ADNI dataset. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1007/s00521-021-05799-w | NEURAL COMPUTING & APPLICATIONS |
Keywords | DocType | Volume |
AD-classification, Convolutional neural network (CNN), Magnetic resonance imaging (MRI), Adaptive momentum estimation (Adam), Glorot uniform weight initializer | Journal | 33 |
Issue | ISSN | Citations |
16 | 0941-0643 | 1 |
PageRank | References | Authors |
0.37 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yousry M. AbdulAzeem | 1 | 7 | 2.50 |
| Waleed M. Bahgat | 2 | 1 | 0.37 |
| Mahmoud Badawy | 3 | 1 | 1.72 |