# Diagnosis of Alzheimer's Disease via Multi-modality 3D Convolutional Neural Network

arXiv: https://arxiv.org/abs/1902.09904 · HTML: https://mlsanity.com/paper/1902.09904/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Feb 2019.

## Built on by (most cited first, 15 of 15)

- [A review of deep learning-based information fusion techniques for multimodal medical image classification](https://mlsanity.com/paper/2404.15022/) (2024)
- [Self-Supervised Longitudinal Neighbourhood Embedding](https://mlsanity.com/paper/2103.03840/) (2021)
- [Is a PET all you need? A multi-modal study for Alzheimer's disease using 3D CNNs](https://mlsanity.com/paper/2207.02094/) (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)
- [Brain Foundation Models with Hypergraph Dynamic Adapter for Brain Disease Analysis](https://mlsanity.com/paper/2505.00627/) (2025)
- [Deep neural network heatmaps capture Alzheimer's disease patterns reported in a large meta-analysis of neuroimaging studies](https://mlsanity.com/paper/2207.11352/) (2022)
- [DiaMond: Dementia Diagnosis with Multi-Modal Vision Transformers Using MRI and PET](https://mlsanity.com/paper/2410.23219/) (2024)
- [Towards better Interpretable and Generalizable AD detection using Collective Artificial Intelligence](https://mlsanity.com/paper/2206.03247/) (2022)
- [A Progressive Single-Modality to Multi-Modality Classification Framework for Alzheimer's Disease Sub-type Diagnosis](https://mlsanity.com/paper/2407.18466/) (2024)
- [A Comparative Analysis on Metaheuristic Algorithms Based Vision Transformer Model for Early Detection of Alzheimer's Disease](https://mlsanity.com/paper/2401.09795/) (2024)
- [Image-Based Alzheimer's Disease Detection Using Pretrained Convolutional Neural Network Models](https://mlsanity.com/paper/2502.05815/) (2025)
- [Multimodal Identification of Alzheimer's Disease: A Review](https://mlsanity.com/paper/2311.12842/) (2023)
- [HyperMM : Robust Multimodal Learning with Varying-sized Inputs](https://mlsanity.com/paper/2407.20768/) (2024)
- [Enhancing Psychologists' Understanding through Explainable Deep Learning Framework for ADHD Diagnosis](https://mlsanity.com/paper/2602.02535/) (2026)
- [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 one of the most concerned neurodegenerative diseases. In the last decade, studies on AD diagnosis attached great significance to artificial intelligence (AI)-based diagnostic algorithms. Among the diverse modality imaging data, T1-weighted MRI and 18F-FDGPET are widely researched for this task. In this paper, we propose a novel convolutional neural network (CNN) to fuse the multi-modality information including T1-MRI and FDG-PDT images around the hippocampal area for the diagnosis of AD. Different from the traditional machine learning algorithms, this method does not require manually extracted features, and utilizes the stateof-art 3D image-processing CNNs to learn features for the diagnosis and prognosis of AD. To validate the performance of the proposed network, we trained the classifier with paired T1-MRI and FDG-PET images using the ADNI datasets, including 731 Normal (NL) subjects, 647 AD subjects, 441 stable MCI (sMCI) subjects and 326 progressive MCI (pMCI) subjects. We obtained the maximal accuracies of 90.10% for NL/AD task, 87.46% for NL/pMCI task, and 76.90% for sMCI/pMCI task. The proposed framework yields comparative results against state-of-the-art approaches. Moreover, the experimental results have demonstrated that (1) segmentation is not a prerequisite by using CNN, (2) the hippocampal area provides enough information to give a reference to AD diagnosis. Keywords: Alzheimer's Disease, Multi-modality, Image Classification, CNN, Deep Learning, Hippocampal