An Ensemble of Deep Convolutional Neural Networks for Alzheimer's Disease Detection and Classification
This work addresses earlier detection of Alzheimer's disease for improved treatment, but it is incremental as it applies an existing ensemble method to a specific medical imaging task.
The paper tackles Alzheimer's disease detection and classification using brain MRI data by developing an ensemble of deep convolutional neural networks, achieving superior performance on the OASIS dataset.
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.