# Predicting Alzheimer's disease: a neuroimaging study with 3D convolutional neural networks

arXiv: https://arxiv.org/abs/1502.02506 · HTML: https://mlsanity.com/paper/1502.02506/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Feb 2015.

## Built on by (most cited first, 17 of 17)

- [Is a PET all you need? A multi-modal study for Alzheimer's disease using 3D CNNs](https://mlsanity.com/paper/2207.02094/) (2022)
- [Deep Learning in current Neuroimaging: a multivariate approach with power and type I error control but arguable generalization ability](https://mlsanity.com/paper/2103.16685/) (2021)
- [Promises and pitfalls of deep neural networks in neuroimaging-based psychiatric research](https://mlsanity.com/paper/2301.08525/) (2023)
- [Biceph-Net: A robust and lightweight framework for the diagnosis of Alzheimer's disease using 2D-MRI scans and deep similarity learning](https://mlsanity.com/paper/2203.12197/) (2022)
- [Multi-scale fMRI time series analysis for understanding neurodegeneration in MCI](https://mlsanity.com/paper/2402.02811/) (2024)
- [Enhancing MRI-Based Classification of Alzheimer's Disease with Explainable 3D Hybrid Compact Convolutional Transformers](https://mlsanity.com/paper/2403.16175/) (2024)
- [Looking deeper into interpretable deep learning in neuroimaging: a comprehensive survey](https://mlsanity.com/paper/2307.09615/) (2023)
- [BRAIN2DEPTH: Lightweight CNN Model for Classification of Cognitive States from EEG Recordings](https://mlsanity.com/paper/2106.06688/) (2021)
- [Deep Learning in Healthcare: An In-Depth Analysis](https://mlsanity.com/paper/2302.10904/) (2023)
- [Automatic detection of impact craters on Al foils from the Stardust interstellar dust collector using convolutional neural networks](https://mlsanity.com/paper/2103.09673/) (2021)
- [An overview of deep learning in medical imaging](https://mlsanity.com/paper/2202.08546/) (2022)
- [Transfer Learning and Class Decomposition for Detecting the Cognitive Decline of Alzheimer Disease](https://mlsanity.com/paper/2301.13504/) (2023)
- [Medical Image Analysis using Deep Relational Learning](https://mlsanity.com/paper/2303.16099/) (2023)
- [Convolutional Neural Networks for Neuroimaging in Parkinson's Disease: Is Preprocessing Needed?](https://mlsanity.com/paper/2311.12561/) (2023)
- [Multimodal Identification of Alzheimer's Disease: A Review](https://mlsanity.com/paper/2311.12842/) (2023)
- [Alzheimer's Disease Diagnosis using a Multimodal Approach with 3D MRI and PET](https://mlsanity.com/paper/2606.20037/) (2026)
- [Comparative Study of Anatomical and Learned Features in AI Models for Structural Brain MRI](https://mlsanity.com/paper/2609.06807/) (2026)

## Abstract (arXiv)

Pattern recognition methods using neuroimaging data for the diagnosis of Alzheimer's disease have been the subject of extensive research in recent years. In this paper, we use deep learning methods, and in particular sparse autoencoders and 3D convolutional neural networks, to build an algorithm that can predict the disease status of a patient, based on an MRI scan of the brain. We report on experiments using the ADNI data set involving 2,265 historical scans. We demonstrate that 3D convolutional neural networks outperform several other classifiers reported in the literature and produce state-of-art results.