Input Agnostic Deep Learning for Alzheimer's Disease Classification Using Multimodal MRI Images

• URL: http://arxiv.org/abs/2107.08673v1
• Date: Mon, 19 Jul 2021 08:19:34 GMT
• Title: Input Agnostic Deep Learning for Alzheimer's Disease Classification Using Multimodal MRI Images
• Authors: Aidana Massalimova and Huseyin Atakan Varol
• Abstract summary: Alzheimer's disease (AD) is a progressive brain disorder that causes memory and functional impairments. In this work, we utilize a multi-modal deep learning approach in classifying normal cognition, mild cognitive impairment and AD classes.
• Score: 1.4848525762485871
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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