Diagnosis plays an important role in preventing progress and treating the Alzheimer’s disease (AD). This paper proposed to predict the AD with a convolutional neural network (CNN), which can learn generic features capturing AD biomarkers. In particular, we extract some specific brain regions from structural MRI and apply MR features from the brain regions to detect AD patients in CNN framework, achieving accuracy up to 99% and outperforming some other classifiers from other studies.
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