NDLI: Ensemble of deep convolutional neural networks based multi-modality images for Alzheimer's disease diagnosis

Content Provider	IET Digital Library
Author	Fang, Xusheng Liu, Zhenbing Xu, Mingchang
Abstract	Alzheimer's disease (AD) is one of the most common progressive neurodegenerative diseases. Structural magnetic resonance imaging (MRI) would provide abundant information on the anatomical structure of human organs. Fluorodeoxy-glucose positron emission tomography (PET) obtains the metabolic activity of the brain. Previous studies have demonstrated that multi-modality images could contribute to improve diagnosis of AD. However, these methods need to extract the handcrafted features that demand domain specific knowledge and image processing stage is time consuming. In order to tackle these problems, in this study, the authors propose a novel framework that ensembles three state-of-the-art deep convolutional neural networks (DCNNs) with multi-modality images for AD classification. In detail, they extract some slices from each subject of each modality, and every DCNN generates a probabilistic score for the input slices. Furthermore, a ‘dropout’ mechanism is introduced to discard low discrimination slices of the category probabilities. Then average reserved slices of each subject are acquired as a new feature. Finally, they train the Adaboost ensemble classifier based on single decision tree classifier with the MRI and PET probabilistic scores of each DCNN. Evaluations on Alzheimer's Disease Neuroimaging Initiative database show that the proposed algorithm has better performance compared to existing method, the algorithm proposed in this study significantly improved the classification accuracy.
Starting Page	318
Ending Page	326
Page Count	9
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 2, Feb (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/2
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2019.0617
Journal	IET Image Processing
Publisher Date	2019-10-22
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Adaboost Ensemble Classifier Alzheimer's Disease Classification Alzheimer's Disease Diagnosis Alzheimer's Disease Neuroimaging Initiative Database Anatomical Structure Biology And Medical Computing Biomedical Magnetic Resonance Imaging Biomedical MRI Biophysics of Neurophysiological Processes Brain Classification Accuracy Common Progressive Neurodegenerative Diseases Computer Vision And Image Processing Technique Convolutional Neural Nets Decision Tree Deep Convolutional Neural Network Diseases Feature Extraction Fluorodeoxy-glucose Positron Emission Tomography Image Classification Image Processing Stage Image Recognition Learning in AI Medical Image Processing Medical Magnetic Resonance Imaging And Spectroscopy Multimodality Image Neural Computing Technique Neurophysiology Nuclear Medicine, Emission Tomography Patient Diagnostic Method And Instrumentation Positron Emission Tomography Single Decision Tree Classifier Spectroscopy Structural Magnetic Resonance Imaging
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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