# Classification of Alzheimer's Disease using fMRI Data and Deep Learning Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1603.08631 · HTML: https://mlsanity.com/paper/1603.08631/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Mar 2016.

## Built on by (most cited first, 8 of 8)

- [BolT: Fused Window Transformers for fMRI Time Series Analysis](https://mlsanity.com/paper/2205.11578/) (2022)
- [Applications of Generative Adversarial Networks in Neuroimaging and Clinical Neuroscience](https://mlsanity.com/paper/2206.07081/) (2022)
- [Looking deeper into interpretable deep learning in neuroimaging: a comprehensive survey](https://mlsanity.com/paper/2307.09615/) (2023)
- [Formulating A Strategic Plan Based On Statistical Analyses And Applications For Financial Companies Through A Real-World Use Case](https://mlsanity.com/paper/2307.04778/) (2023)
- [ChatGPT Application In Summarizing An Evolution Of Deep Learning Techniques In Imaging: A Qualitative Study](https://mlsanity.com/paper/2312.03723/) (2023)
- [A Multi-view Impartial Decision Network for Frontotemporal Dementia Diagnosis](https://mlsanity.com/paper/2307.04981/) (2023)
- [Using Graph Convolutional Networks to Address fMRI Small Data Problems](https://mlsanity.com/paper/2502.17489/) (2025)
- [Alzheimer's Disease Diagnosis using a Multimodal Approach with 3D MRI and PET](https://mlsanity.com/paper/2606.20037/) (2026)

## Abstract (arXiv)

Over the past decade, machine learning techniques especially predictive modeling and pattern recognition in biomedical sciences from drug delivery system to medical imaging has become one of the important methods which are assisting researchers to have deeper understanding of entire issue and to solve complex medical problems. Deep learning is power learning machine learning algorithm in classification while extracting high-level features. In this paper, we used convolutional neural network to classify Alzheimer's brain from normal healthy brain. The importance of classifying this kind of medical data is to potentially develop a predict model or system in order to recognize the type disease from normal subjects or to estimate the stage of the disease. Classification of clinical data such as Alzheimer's disease has been always challenging and most problematic part has been always selecting the most discriminative features. Using Convolutional Neural Network (CNN) and the famous architecture LeNet-5, we successfully classified functional MRI data of Alzheimer's subjects from normal controls where the accuracy of test data on trained data reached 96.85%. This experiment suggests us the shift and scale invariant features extracted by CNN followed by deep learning classification is most powerful method to distinguish clinical data from healthy data in fMRI. This approach also enables us to expand our methodology to predict more complicated systems.