# An Ensemble of Deep Convolutional Neural Networks for Alzheimer's Disease Detection and Classification

arXiv: https://arxiv.org/abs/1712.01675 · HTML: https://mlsanity.com/paper/1712.01675/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Dec 2017.

## Built on by (most cited first, 2 of 2)

- [Deep Convolutional Neural Network based Classification of Alzheimer's Disease using MRI data](https://mlsanity.com/paper/2101.02876/) (2021)
- [Neural Networks for Infectious Diseases Detection: Prospects and Challenges](https://mlsanity.com/paper/2112.03571/) (2021)

## Abstract (arXiv)

Alzheimer's Disease destroys brain cells causing people to lose their memory, mental functions and ability to continue daily activities. It is a severe neurological brain disorder which is not curable, but earlier detection of Alzheimer's Disease can help for proper treatment and to prevent brain tissue damage. Detection and classification of Alzheimer's Disease (AD) is challenging because sometimes the signs that distinguish Alzheimer's Disease MRI data can be found in normal healthy brain MRI data of older people. Moreover, there are relatively small amount of dataset available to train the automated Alzheimer's Disease detection and classification model. In this paper, we present a novel Alzheimer's Disease detection and classification model using brain MRI data analysis. We develop an ensemble of deep convolutional neural networks and demonstrate superior performance on the Open Access Series of Imaging Studies (OASIS) dataset.