# Automatic Detection of Alzheimer's Disease with Multi-Modal Fusion of Clinical MRI Scans

arXiv: https://arxiv.org/abs/2311.18245 · HTML: https://mlsanity.com/paper/2311.18245/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 27 references that are in the map.
- First posted on arXiv 30 Nov 2023.

## Builds on (most cited first, 6 of 6)

- [Multimodal Deep Learning](https://mlsanity.com/paper/2301.04856/) (2023)
- [TransMed: Transformers Advance Multi-modal Medical Image Classification](https://mlsanity.com/paper/2103.05940/) (2021)
- [Learning Common and Specific Features for RGB-D Semantic Segmentation with Deconvolutional Networks](https://mlsanity.com/paper/1608.01082/) (2016)
- [Deep Surface Normal Estimation with Hierarchical RGB-D Fusion](https://mlsanity.com/paper/1904.03405/) (2019)
- [Modality Compensation Network: Cross-Modal Adaptation for Action Recognition](https://mlsanity.com/paper/2001.11657/) (2020)
- [On the design of convolutional neural networks for automatic detection of Alzheimer's disease](https://mlsanity.com/paper/1911.03740/) (2019)

## Built on by (most cited first, 1 of 1)

- [Cerebra: A Multidisciplinary AI Board for Multimodal Dementia Characterization and Risk Assessment](https://mlsanity.com/paper/2603.21597/) (2026)

## Abstract (arXiv)

The aging population of the U.S. drives the prevalence of Alzheimer's disease. Brookmeyer et al. forecasts approximately 15 million Americans will have either clinical AD or mild cognitive impairment by 2060. In response to this urgent call, methods for early detection of Alzheimer's disease have been developed for prevention and pre-treatment. Notably, literature on the application of deep learning in the automatic detection of the disease has been proliferating. This study builds upon previous literature and maintains a focus on leveraging multi-modal information to enhance automatic detection. We aim to predict the stage of the disease - Cognitively Normal (CN), Mildly Cognitive Impairment (MCI), and Alzheimer's Disease (AD), based on two different types of brain MRI scans. We design an AlexNet-based deep learning model that learns the synergy of complementary information from both T1 and FLAIR MRI scans.