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

arXiv: https://arxiv.org/abs/2107.08673 · HTML: https://mlsanity.com/paper/2107.08673/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 17 references that are in the map.
- First posted on arXiv 19 Jul 2021.

## Builds on (most cited first, 1 of 1)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)

## Built on by (most cited first, 4 of 4)

- [A review of deep learning-based information fusion techniques for multimodal medical image classification](https://mlsanity.com/paper/2404.15022/) (2024)
- [Medical Multimodal Foundation Models in Clinical Diagnosis and Treatment: Applications, Challenges, and Future Directions](https://mlsanity.com/paper/2412.02621/) (2024)
- [Diagnosing Alzheimer's Disease using Early-Late Multimodal Data Fusion with Jacobian Maps](https://mlsanity.com/paper/2310.16936/) (2023)
- [Unmasking Dementia Detection by Masking Input Gradients: A JSM Approach to Model Interpretability and Precision](https://mlsanity.com/paper/2402.16008/) (2024)

## Abstract (arXiv)

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.