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

TL;DR

This paper introduces an ensemble of deep convolutional neural networks for detecting and classifying Alzheimer's Disease from MRI data, achieving superior accuracy on the OASIS dataset.

Contribution

It proposes a novel ensemble approach combining multiple CNNs specifically designed for Alzheimer's detection, addressing data scarcity and improving classification performance.

Findings

Superior accuracy on the OASIS dataset

Effective handling of small datasets

Enhanced detection and classification performance

Abstract

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.