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Detection of Alzheimer’s disease by displacement field and machine learning
Aim. Alzheimer’s disease (AD) is a chronic neurodegenerative disease. Recently, computer scientists have developed various methods for early detection based on computer vision and machine learning techniques. Method. In this study, we proposed a novel AD detection method by displacement field (DF) e...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4579022/ https://www.ncbi.nlm.nih.gov/pubmed/26401461 http://dx.doi.org/10.7717/peerj.1251 |
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author | Zhang, Yudong Wang, Shuihua |
author_facet | Zhang, Yudong Wang, Shuihua |
author_sort | Zhang, Yudong |
collection | PubMed |
description | Aim. Alzheimer’s disease (AD) is a chronic neurodegenerative disease. Recently, computer scientists have developed various methods for early detection based on computer vision and machine learning techniques. Method. In this study, we proposed a novel AD detection method by displacement field (DF) estimation between a normal brain and an AD brain. The DF was treated as the AD-related features, reduced by principal component analysis (PCA), and finally fed into three classifiers: support vector machine (SVM), generalized eigenvalue proximal SVM (GEPSVM), and twin SVM (TSVM). The 10-fold cross validation repeated 50 times. Results. The results showed the “DF + PCA + TSVM” achieved the accuracy of 92.75 ± 1.77, sensitivity of 90.56 ± 1.15, specificity of 93.37 ± 2.05, and precision of 79.61 ± 2.21. This result is better than or comparable with not only the other proposed two methods, but also ten state-of-the-art methods. Besides, our method discovers the AD is related to following brain regions disclosed in recent publications: Angular Gyrus, Anterior Cingulate, Cingulate Gyrus, Culmen, Cuneus, Fusiform Gyrus, Inferior Frontal Gyrus, Inferior Occipital Gyrus, Inferior Parietal Lobule, Inferior Semi-Lunar Lobule, Inferior Temporal Gyrus, Insula, Lateral Ventricle, Lingual Gyrus, Medial Frontal Gyrus, Middle Frontal Gyrus, Middle Occipital Gyrus, Middle Temporal Gyrus, Paracentral Lobule, Parahippocampal Gyrus, Postcentral Gyrus, Posterior Cingulate, Precentral Gyrus, Precuneus, Sub-Gyral, Superior Parietal Lobule, Superior Temporal Gyrus, Supramarginal Gyrus, and Uncus. Conclusion. The displacement filed is effective in detection of AD and related brain-regions. |
format | Online Article Text |
id | pubmed-4579022 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-45790222015-09-23 Detection of Alzheimer’s disease by displacement field and machine learning Zhang, Yudong Wang, Shuihua PeerJ Neuroscience Aim. Alzheimer’s disease (AD) is a chronic neurodegenerative disease. Recently, computer scientists have developed various methods for early detection based on computer vision and machine learning techniques. Method. In this study, we proposed a novel AD detection method by displacement field (DF) estimation between a normal brain and an AD brain. The DF was treated as the AD-related features, reduced by principal component analysis (PCA), and finally fed into three classifiers: support vector machine (SVM), generalized eigenvalue proximal SVM (GEPSVM), and twin SVM (TSVM). The 10-fold cross validation repeated 50 times. Results. The results showed the “DF + PCA + TSVM” achieved the accuracy of 92.75 ± 1.77, sensitivity of 90.56 ± 1.15, specificity of 93.37 ± 2.05, and precision of 79.61 ± 2.21. This result is better than or comparable with not only the other proposed two methods, but also ten state-of-the-art methods. Besides, our method discovers the AD is related to following brain regions disclosed in recent publications: Angular Gyrus, Anterior Cingulate, Cingulate Gyrus, Culmen, Cuneus, Fusiform Gyrus, Inferior Frontal Gyrus, Inferior Occipital Gyrus, Inferior Parietal Lobule, Inferior Semi-Lunar Lobule, Inferior Temporal Gyrus, Insula, Lateral Ventricle, Lingual Gyrus, Medial Frontal Gyrus, Middle Frontal Gyrus, Middle Occipital Gyrus, Middle Temporal Gyrus, Paracentral Lobule, Parahippocampal Gyrus, Postcentral Gyrus, Posterior Cingulate, Precentral Gyrus, Precuneus, Sub-Gyral, Superior Parietal Lobule, Superior Temporal Gyrus, Supramarginal Gyrus, and Uncus. Conclusion. The displacement filed is effective in detection of AD and related brain-regions. PeerJ Inc. 2015-09-17 /pmc/articles/PMC4579022/ /pubmed/26401461 http://dx.doi.org/10.7717/peerj.1251 Text en © 2015 Zhang and Wang http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited. |
spellingShingle | Neuroscience Zhang, Yudong Wang, Shuihua Detection of Alzheimer’s disease by displacement field and machine learning |
title | Detection of Alzheimer’s disease by displacement field and machine learning |
title_full | Detection of Alzheimer’s disease by displacement field and machine learning |
title_fullStr | Detection of Alzheimer’s disease by displacement field and machine learning |
title_full_unstemmed | Detection of Alzheimer’s disease by displacement field and machine learning |
title_short | Detection of Alzheimer’s disease by displacement field and machine learning |
title_sort | detection of alzheimer’s disease by displacement field and machine learning |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4579022/ https://www.ncbi.nlm.nih.gov/pubmed/26401461 http://dx.doi.org/10.7717/peerj.1251 |
work_keys_str_mv | AT zhangyudong detectionofalzheimersdiseasebydisplacementfieldandmachinelearning AT wangshuihua detectionofalzheimersdiseasebydisplacementfieldandmachinelearning |