  Alzheimer's disease (AD) is a progressive brain disorder that causes memory
and functional impairments. The advances in machine learning and publicly
available medical datasets initiated multiple studies in AD diagnosis. In this
work, we utilize a multi-modal deep learning approach in classifying normal
cognition, mild cognitive impairment and AD classes on the basis of structural
MRI and diffusion tensor imaging (DTI) scans from the OASIS-3 dataset. In
addition to a conventional multi-modal network, we also present an input
agnostic architecture that allows diagnosis with either sMRI or DTI scan, which
distinguishes our method from previous multi-modal machine learning-based
methods. The results show that the input agnostic model achieves 0.96 accuracy
when both structural MRI and DTI scans are provided as inputs.
