Cargando…

A Deep Siamese Convolution Neural Network for Multi-Class Classification of Alzheimer Disease

Alzheimer’s disease (AD) may cause damage to the memory cells permanently, which results in the form of dementia. The diagnosis of Alzheimer’s disease at an early stage is a problematic task for researchers. For this, machine learning and deep convolutional neural network (CNN) based approaches are...

Descripción completa

Detalles Bibliográficos
Autores principales: Mehmood, Atif, Maqsood, Muazzam, Bashir, Muzaffar, Shuyuan, Yang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7071616/
https://www.ncbi.nlm.nih.gov/pubmed/32033462
http://dx.doi.org/10.3390/brainsci10020084
_version_ 1783506242526773248
author Mehmood, Atif
Maqsood, Muazzam
Bashir, Muzaffar
Shuyuan, Yang
author_facet Mehmood, Atif
Maqsood, Muazzam
Bashir, Muzaffar
Shuyuan, Yang
author_sort Mehmood, Atif
collection PubMed
description Alzheimer’s disease (AD) may cause damage to the memory cells permanently, which results in the form of dementia. The diagnosis of Alzheimer’s disease at an early stage is a problematic task for researchers. For this, machine learning and deep convolutional neural network (CNN) based approaches are readily available to solve various problems related to brain image data analysis. In clinical research, magnetic resonance imaging (MRI) is used to diagnose AD. For accurate classification of dementia stages, we need highly discriminative features obtained from MRI images. Recently advanced deep CNN-based models successfully proved their accuracy. However, due to a smaller number of image samples available in the datasets, there exist problems of over-fitting hindering the performance of deep learning approaches. In this research, we developed a Siamese convolutional neural network (SCNN) model inspired by VGG-16 (also called Oxford Net) to classify dementia stages. In our approach, we extend the insufficient and imbalanced data by using augmentation approaches. Experiments are performed on a publicly available dataset open access series of imaging studies (OASIS), by using the proposed approach, an excellent test accuracy of 99.05% is achieved for the classification of dementia stages. We compared our model with the state-of-the-art models and discovered that the proposed model outperformed the state-of-the-art models in terms of performance, efficiency, and accuracy.
format Online
Article
Text
id pubmed-7071616
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-70716162020-03-19 A Deep Siamese Convolution Neural Network for Multi-Class Classification of Alzheimer Disease Mehmood, Atif Maqsood, Muazzam Bashir, Muzaffar Shuyuan, Yang Brain Sci Article Alzheimer’s disease (AD) may cause damage to the memory cells permanently, which results in the form of dementia. The diagnosis of Alzheimer’s disease at an early stage is a problematic task for researchers. For this, machine learning and deep convolutional neural network (CNN) based approaches are readily available to solve various problems related to brain image data analysis. In clinical research, magnetic resonance imaging (MRI) is used to diagnose AD. For accurate classification of dementia stages, we need highly discriminative features obtained from MRI images. Recently advanced deep CNN-based models successfully proved their accuracy. However, due to a smaller number of image samples available in the datasets, there exist problems of over-fitting hindering the performance of deep learning approaches. In this research, we developed a Siamese convolutional neural network (SCNN) model inspired by VGG-16 (also called Oxford Net) to classify dementia stages. In our approach, we extend the insufficient and imbalanced data by using augmentation approaches. Experiments are performed on a publicly available dataset open access series of imaging studies (OASIS), by using the proposed approach, an excellent test accuracy of 99.05% is achieved for the classification of dementia stages. We compared our model with the state-of-the-art models and discovered that the proposed model outperformed the state-of-the-art models in terms of performance, efficiency, and accuracy. MDPI 2020-02-05 /pmc/articles/PMC7071616/ /pubmed/32033462 http://dx.doi.org/10.3390/brainsci10020084 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Mehmood, Atif
Maqsood, Muazzam
Bashir, Muzaffar
Shuyuan, Yang
A Deep Siamese Convolution Neural Network for Multi-Class Classification of Alzheimer Disease
title A Deep Siamese Convolution Neural Network for Multi-Class Classification of Alzheimer Disease
title_full A Deep Siamese Convolution Neural Network for Multi-Class Classification of Alzheimer Disease
title_fullStr A Deep Siamese Convolution Neural Network for Multi-Class Classification of Alzheimer Disease
title_full_unstemmed A Deep Siamese Convolution Neural Network for Multi-Class Classification of Alzheimer Disease
title_short A Deep Siamese Convolution Neural Network for Multi-Class Classification of Alzheimer Disease
title_sort deep siamese convolution neural network for multi-class classification of alzheimer disease
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7071616/
https://www.ncbi.nlm.nih.gov/pubmed/32033462
http://dx.doi.org/10.3390/brainsci10020084
work_keys_str_mv AT mehmoodatif adeepsiameseconvolutionneuralnetworkformulticlassclassificationofalzheimerdisease
AT maqsoodmuazzam adeepsiameseconvolutionneuralnetworkformulticlassclassificationofalzheimerdisease
AT bashirmuzaffar adeepsiameseconvolutionneuralnetworkformulticlassclassificationofalzheimerdisease
AT shuyuanyang adeepsiameseconvolutionneuralnetworkformulticlassclassificationofalzheimerdisease
AT mehmoodatif deepsiameseconvolutionneuralnetworkformulticlassclassificationofalzheimerdisease
AT maqsoodmuazzam deepsiameseconvolutionneuralnetworkformulticlassclassificationofalzheimerdisease
AT bashirmuzaffar deepsiameseconvolutionneuralnetworkformulticlassclassificationofalzheimerdisease
AT shuyuanyang deepsiameseconvolutionneuralnetworkformulticlassclassificationofalzheimerdisease