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• W4322771010 abstract "Magnetic Resonance Imaging helps in detecting brain atrophy caused by Alzheimer’s disease (AD). Morphological changes start to occur in the temporal lobe of the brain in persons affected by AD. A 3D Convolutional Neural Network is proposed that focuses on the temporal lobe of the brain for AD classification from normal controls. By investigating the temporal region of the grey matter in the brain instead of the entire brain region high performance is obtained with an accuracy of 88.06%, a sensitivity of 94.03%, and a specificity of 82.09% in classifying the MRI volumes of Alzheimer’s Disease Neuroimaging Initiative (ADNI-2) dataset. When Apolipoprotein ε4 allele information and mini-mental state examination scores are used with the MRI volumes, an accuracy of 89.55%, a sensitivity of 82.09% and a specificity of 97.01% for ADNI-2 MRI volumes are achieved. When tested on ADNI-3 MRI volumes the proposed method resulted in an accuracy of 90.14%, a sensitivity of 79.17% and a specificity of 92.37%." @default.
• W4322771010 created "2023-03-03" @default.
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• W4322771010 creator A5081914658 @default.
• W4322771010 date "2023-03-02" @default.
• W4322771010 modified "2023-09-25" @default.
• W4322771010 title "Detection of Alzheimer’s disease from temporal lobe grey matter slices using 3D CNN" @default.
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• W4322771010 doi "https://doi.org/10.1080/13682199.2023.2173548" @default.
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