Deep Learning Framework for Alzheimer's Disease Diagnosis via 3D-CNN and FSBi-LSTM

Alzheimer's disease (AD) is an irreversible progressive neurodegenerative disorder. Mild cognitive impairment (MCI) is the prodromal state of AD, which is further classified into a progressive state (i.e., pMCI) and a stable state (i.e., sMCI). With the development of deep learning, the convolu...

Full description

Saved in:
Bibliographic Details
Published in:IEEE access 2019, Vol.7, p.63605-63618
Main Authors: Feng, Chiyu, Elazab, Ahmed, Yang, Peng, Wang, Tianfu, Zhou, Feng, Hu, Huoyou, Xiao, Xiaohua, Lei, Baiying
Format: Article
Language:English
Subjects:
3D-CNN
Algorithms
Alzheimer's disease
Artificial neural networks
Deep learning
Diagnosis
Diseases
Feature extraction
Feature maps
FSBi-LSTM
Kernel
Machine learning
Magnetic resonance imaging
Medical imaging
multi-modal fusion
Object recognition
Positron emission
Spatial data
Three-dimensional displays
Tomography
Two dimensional displays
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Alzheimer's disease (AD) is an irreversible progressive neurodegenerative disorder. Mild cognitive impairment (MCI) is the prodromal state of AD, which is further classified into a progressive state (i.e., pMCI) and a stable state (i.e., sMCI). With the development of deep learning, the convolutional neural networks (CNNs) have made great progress in image recognition using magnetic resonance imaging (MRI) and positron emission tomography (PET) for AD diagnosis. However, due to the limited availability of these imaging data, it is still challenging to effectively use CNNs for AD diagnosis. Toward this end, we design a novel deep learning framework. Specifically, the virtues of 3D-CNN and fully stacked bidirectional long short-term memory (FSBi-LSTM) are exploited in our framework. First, we design a 3D-CNN architecture to derive deep feature representation from both MRI and PET. Then, we apply FSBi-LSTM on the hidden spatial information from deep feature maps to further improve its performance. Finally, we validate our method on the AD neuroimaging initiative (ADNI) dataset. Our method achieves average accuracies of 94.82%, 86.36%, and 65.35% for differentiating AD from normal control (NC), pMCI from NC, and sMCI from NC, respectively, and outperforms the related algorithms in the literature.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2019.2913847