Input Agnostic Deep Learning for Alzheimer's Disease Classification Using Multimodal MRI Images
This addresses Alzheimer's diagnosis for medical applications, but it is incremental as it builds on existing multimodal deep learning approaches.
The paper tackled Alzheimer's disease classification using multimodal MRI images, achieving 0.96 accuracy with an input agnostic model that works with either structural MRI or DTI scans.
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.