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Alzheimer disease diagnosis for magnetic resonance brain images using deep learning neural networks

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Abstract

In this work, a Deep Convolutional Neural Network (DCNN) framework for Alzheimer’s Disease (AD) diagnosis based on brain Magnetic Resonance Imaging (MRI) scans is presented. A multiclass DCNN classifier is used to discriminate between Normal Controls (NC), Mild Cognitive Impairment (MCI), and AD. The Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset was used to train and test the proposed DCNN. Different train-test ratios have been examined. Average accuracies of 100% for AD/NC, 92.93% for NC/MCI, and 99.21% for AD/MCI were obtained. The proposed system achieved an average accuracy of 93.86% for a three-way AD/MCI/NC classification. To further examine the proposed system performance, Receiver Operation Characteristics (ROC) analysis and Confusion Matrix (CM) were also used. For certain scenarios, the Area Under ROC Curve (AUC) values of 1, 1, and 0.989 were obtained for AD, NC, and MCI, respectively. The results show higher metrics compared to previously published studies concerning AD diagnosis.

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Authors and Affiliations

  1. Informatics Research Institute (IRI), City of Scientific Research and Technological Applications (SRTA-City), Alexandria, Egypt

    Heba M. Ahmed

  2. Engineering Department, Nuclear Research Center, Egyptian Atomic Energy Authority, Cairo, Egypt

    Zeinab F. Elsharkawy

  3. Department of Electronics and Electrical Communication Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, 32952, Egypt

    Ahmed S. Elkorany

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  1. Heba M. Ahmed
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  2. Zeinab F. Elsharkawy
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  3. Ahmed S. Elkorany
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Correspondence to Zeinab F. Elsharkawy.

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Ahmed, H.M., Elsharkawy, Z.F. & Elkorany, A.S. Alzheimer disease diagnosis for magnetic resonance brain images using deep learning neural networks. Multimed Tools Appl 82, 17963–17977 (2023). https://doi.org/10.1007/s11042-022-14203-1

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  • DOI: https://doi.org/10.1007/s11042-022-14203-1

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