# Multimodal and Multiscale Deep Neural Networks for the Early Diagnosis of Alzheimer's Disease using structural MR and FDG-PET images

arXiv: https://arxiv.org/abs/1710.04782 · HTML: https://mlsanity.com/paper/1710.04782/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Oct 2017.

## Built on by (most cited first, 9 of 9)

- [A review of deep learning-based information fusion techniques for multimodal medical image classification](https://mlsanity.com/paper/2404.15022/) (2024)
- [Clinically-Inspired Multi-Agent Transformers for Disease Trajectory Forecasting from Multimodal Data](https://mlsanity.com/paper/2210.13889/) (2022)
- [Multi-modal Graph Neural Network for Early Diagnosis of Alzheimer's Disease from sMRI and PET Scans](https://mlsanity.com/paper/2307.16366/) (2023)
- [DiaMond: Dementia Diagnosis with Multi-Modal Vision Transformers Using MRI and PET](https://mlsanity.com/paper/2410.23219/) (2024)
- [Multimodal Identification of Alzheimer's Disease: A Review](https://mlsanity.com/paper/2311.12842/) (2023)
- [Alzheimer's Magnetic Resonance Imaging Classification Using Deep and Meta-Learning Models](https://mlsanity.com/paper/2405.12126/) (2024)
- [Early diagnosis of Alzheimer's disease from MRI images with deep learning model](https://mlsanity.com/paper/2409.18814/) (2024)
- [Alzheimer's Disease Brain Network Mining](https://mlsanity.com/paper/2512.17276/) (2025)
- [Alzheimer's Disease Diagnosis using a Multimodal Approach with 3D MRI and PET](https://mlsanity.com/paper/2606.20037/) (2026)

## Abstract (arXiv)

Alzheimer's Disease (AD) is a progressive neurodegenerative disease. Amnestic mild cognitive impairment (MCI) is a common first symptom before the conversion to clinical impairment where the individual becomes unable to perform activities of daily living independently. Although there is currently no treatment available, the earlier a conclusive diagnosis is made, the earlier the potential for interventions to delay or perhaps even prevent progression to full-blown AD. Neuroimaging scans acquired from MRI and metabolism images obtained by FDG-PET provide in-vivo view into the structure and function (glucose metabolism) of the living brain. It is hypothesized that combining different image modalities could better characterize the change of human brain and result in a more accuracy early diagnosis of AD. In this paper, we proposed a novel framework to discriminate normal control(NC) subjects from subjects with AD pathology (AD and NC, MCI subjects convert to AD in future). Our novel approach utilizing a multimodal and multiscale deep neural network was found to deliver a 85.68% accuracy in the prediction of subjects within 3 years to conversion. Cross validation experiments proved that it has better discrimination ability compared with results in existing published literature.