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) es...
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2015-09-01
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| Series: | PeerJ |
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| Online Access: | https://peerj.com/articles/1251.pdf |
| _version_ | 1827605992678883328 |
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| author | Yudong Zhang Shuihua Wang |
| author_facet | Yudong Zhang Shuihua Wang |
| author_sort | Yudong Zhang |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-09T06:30:41Z |
| format | Article |
| id | doaj.art-186fdc50c50d4b5faee03a0c8643d5ed |
| institution | Directory Open Access Journal |
| issn | 2167-8359 |
| language | English |
| last_indexed | 2024-03-09T06:30:41Z |
| publishDate | 2015-09-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ |
| spelling | doaj.art-186fdc50c50d4b5faee03a0c8643d5ed2023-12-03T11:04:46ZengPeerJ Inc.PeerJ2167-83592015-09-013e125110.7717/peerj.1251Detection of Alzheimer’s disease by displacement field and machine learningYudong Zhang0Shuihua Wang1School of Computer Science and Technology, Nanjing Normal University, Nanjing, Jiangsu, ChinaSchool of Electronic Science and Engineering, Nanjing University, Nanjing, Jiangsu, ChinaAim. 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.https://peerj.com/articles/1251.pdfRegion detectionMachine visionGeneralized eigenvalue proximal SVMAlzheimer’s diseaseWhole brain analysisSupport vector machine (SVM) |
| spellingShingle | Yudong Zhang Shuihua Wang Detection of Alzheimer’s disease by displacement field and machine learning PeerJ Region detection Machine vision Generalized eigenvalue proximal SVM Alzheimer’s disease Whole brain analysis Support vector machine (SVM) |
| 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 | Region detection Machine vision Generalized eigenvalue proximal SVM Alzheimer’s disease Whole brain analysis Support vector machine (SVM) |
| url | https://peerj.com/articles/1251.pdf |
| work_keys_str_mv | AT yudongzhang detectionofalzheimersdiseasebydisplacementfieldandmachinelearning AT shuihuawang detectionofalzheimersdiseasebydisplacementfieldandmachinelearning |