Diagnosis of Alzheimer’s Disease with Ensemble Learning Classifier and 3D Convolutional Neural Network

Alzheimer’s disease (AD), the most common type of dementia, is a progressive disease beginning with mild memory loss, possibly leading to loss of the ability to carry on a conversation and respond to environments. It can seriously affect a person’s ability to carry out daily activities. Therefore, e...

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Autores principales:	Peng Zhang, Shukuan Lin, Jianzhong Qiao, Yue Tu
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	Alzheimer’s disease convolutional neural network ensemble learning data denoising Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/ef23e82ff19944889342ed459fd053fc
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spelling	oai:doaj.org-article:ef23e82ff19944889342ed459fd053fc2021-11-25T18:58:06ZDiagnosis of Alzheimer’s Disease with Ensemble Learning Classifier and 3D Convolutional Neural Network10.3390/s212276341424-8220https://doaj.org/article/ef23e82ff19944889342ed459fd053fc2021-11-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/22/7634https://doaj.org/toc/1424-8220Alzheimer’s disease (AD), the most common type of dementia, is a progressive disease beginning with mild memory loss, possibly leading to loss of the ability to carry on a conversation and respond to environments. It can seriously affect a person’s ability to carry out daily activities. Therefore, early diagnosis of AD is conducive to better treatment and avoiding further deterioration of the disease. Magnetic resonance imaging (MRI) has become the main tool for humans to study brain tissues. It can clearly reflect the internal structure of a brain and plays an important role in the diagnosis of Alzheimer’s disease. MRI data is widely used for disease diagnosis. In this paper, based on MRI data, a method combining a 3D convolutional neural network and ensemble learning is proposed to improve the diagnosis accuracy. Then, a data denoising module is proposed to reduce boundary noise. The experimental results on ADNI dataset demonstrate that the model proposed in this paper improves the training speed of the neural network and achieves 95.2% accuracy in AD vs. NC (normal control) task and 77.8% accuracy in sMCI (stable mild cognitive impairment) vs. pMCI (progressive mild cognitive impairment) task in the diagnosis of Alzheimer’s disease.Peng ZhangShukuan LinJianzhong QiaoYue TuMDPI AGarticleAlzheimer’s diseaseconvolutional neural networkensemble learningdata denoisingChemical technologyTP1-1185ENSensors, Vol 21, Iss 7634, p 7634 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Alzheimer’s disease convolutional neural network ensemble learning data denoising Chemical technology TP1-1185
spellingShingle	Alzheimer’s disease convolutional neural network ensemble learning data denoising Chemical technology TP1-1185 Peng Zhang Shukuan Lin Jianzhong Qiao Yue Tu Diagnosis of Alzheimer’s Disease with Ensemble Learning Classifier and 3D Convolutional Neural Network
description	Alzheimer’s disease (AD), the most common type of dementia, is a progressive disease beginning with mild memory loss, possibly leading to loss of the ability to carry on a conversation and respond to environments. It can seriously affect a person’s ability to carry out daily activities. Therefore, early diagnosis of AD is conducive to better treatment and avoiding further deterioration of the disease. Magnetic resonance imaging (MRI) has become the main tool for humans to study brain tissues. It can clearly reflect the internal structure of a brain and plays an important role in the diagnosis of Alzheimer’s disease. MRI data is widely used for disease diagnosis. In this paper, based on MRI data, a method combining a 3D convolutional neural network and ensemble learning is proposed to improve the diagnosis accuracy. Then, a data denoising module is proposed to reduce boundary noise. The experimental results on ADNI dataset demonstrate that the model proposed in this paper improves the training speed of the neural network and achieves 95.2% accuracy in AD vs. NC (normal control) task and 77.8% accuracy in sMCI (stable mild cognitive impairment) vs. pMCI (progressive mild cognitive impairment) task in the diagnosis of Alzheimer’s disease.
format	article
author	Peng Zhang Shukuan Lin Jianzhong Qiao Yue Tu
author_facet	Peng Zhang Shukuan Lin Jianzhong Qiao Yue Tu
author_sort	Peng Zhang
title	Diagnosis of Alzheimer’s Disease with Ensemble Learning Classifier and 3D Convolutional Neural Network
title_short	Diagnosis of Alzheimer’s Disease with Ensemble Learning Classifier and 3D Convolutional Neural Network
title_full	Diagnosis of Alzheimer’s Disease with Ensemble Learning Classifier and 3D Convolutional Neural Network
title_fullStr	Diagnosis of Alzheimer’s Disease with Ensemble Learning Classifier and 3D Convolutional Neural Network
title_full_unstemmed	Diagnosis of Alzheimer’s Disease with Ensemble Learning Classifier and 3D Convolutional Neural Network
title_sort	diagnosis of alzheimer’s disease with ensemble learning classifier and 3d convolutional neural network
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/ef23e82ff19944889342ed459fd053fc
work_keys_str_mv	AT pengzhang diagnosisofalzheimersdiseasewithensemblelearningclassifierand3dconvolutionalneuralnetwork AT shukuanlin diagnosisofalzheimersdiseasewithensemblelearningclassifierand3dconvolutionalneuralnetwork AT jianzhongqiao diagnosisofalzheimersdiseasewithensemblelearningclassifierand3dconvolutionalneuralnetwork AT yuetu diagnosisofalzheimersdiseasewithensemblelearningclassifierand3dconvolutionalneuralnetwork
_version_	1718410499654156288

Diagnosis of Alzheimer’s Disease with Ensemble Learning Classifier and 3D Convolutional Neural Network

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