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Alzheimer disease (AD) is a progressive brain disorder that gradually deprives patients of their basic abilities. Despite the absence of specific treatments, early detection of this neurodegenerative disease can slow its progression and prevent further deterioration. AD can manifest in different stages, including non-demented (nD), mildly demented (mD), moderately demented (moD), and very mildly demented (vmD) which are taken from the Kaggle repository naming [Alzheimer dataset (4 class of Images)]. To classify these stages, we utilized various convolutional network models such as Alz-DenseConvNet, Alz-ResConvNet, Alz-VGGConvNet, Alz-MobileConvNet, and Alz-XceptionConvNet. Both binary and multiclass classification is performed to classify the different stages of AD. After testing and validation, Alz-MobileConvNet was found to be the superior method for multiclass classification with an accuracy (A_cc) of …