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

TL;DR

This paper introduces an input agnostic deep learning model for Alzheimer's disease classification that can diagnose using either structural MRI or DTI scans, achieving high accuracy on the OASIS-3 dataset.

Contribution

The novel input agnostic architecture allows diagnosis with either sMRI or DTI scans, enhancing flexibility over traditional multi-modal models.

Findings

Achieves 0.96 accuracy with both MRI and DTI inputs.

Demonstrates effective classification across three cognitive states.

Introduces a flexible model adaptable to available imaging modalities.

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.