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Structural magnetic resonance imaging (sMRI) has become a prevalent and potent imaging modality for the computer-aided diagnosis (CAD) of neurological diseases like dementia. Recently, a handful of deep learning techniques such as convolutional neural networks (CNNs) have been proposed to diagnose Alzheimer's disease (AD) by learning the atrophy patterns available in sMRIs. Although CNN-based techniques have demonstrated superior performance and characteristics compared to conventional learning-based classifiers, their diagnostic performance still needs to be improved for reliable classification results. The drawback of current CNN-based approaches is the requirement to locate discriminative landmark (LM) locations by identifying regions of interest (ROIs) in sMRIs, thus the performance of the whole framework is highly influenced by the LM detection step. To overcome this issue, we propose a …