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A CNN based framework for classification of Alzheimer’s disease

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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.

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Data used in the preparation of this article were obtained from the Alzheimers disease Database Initiative (ADNI) database. A complete listing of ADNI investigators can be found at:

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Correspondence to Yousry AbdulAzeem.

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Yousry AbdulAzeem, Waleed Bahgat, and Mahmoud Badawy declare that they have no conflict of interest.

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AbdulAzeem, Y., Bahgat, W.M. & Badawy, M. A CNN based framework for classification of Alzheimer’s disease. Neural Comput & Applic 33, 10415–10428 (2021).

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