Detection of Alzheimer’s disease by displacement field and machine learning
Open Access
- 17 September 2015
- Vol. 3, e1251
- https://doi.org/10.7717/peerj.1251
Abstract
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.Keywords
Funding Information
- NSFC (610011024, 61273243, 51407095)
- Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD)
- Program of Natural Science Research of Jiangsu Higher Education Institutions (13KJB460011, 14KJB520021)
- Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing (BM2013006)
- Key Supporting Science and Technology Program (Industry) of Jiangsu Province (BE2012201, BE2014009-3, BE2013012-2)
- Special Funds for Scientific and Technological Achievement Transformation Project in Jiangsu Province (BA2013058)
- Nanjing Normal University Research Foundation for Talented Scholars (2013119XGQ0061, 2014119XGQ0080))
- NIH (P50AG05681, P01 AG03991, R01 AG021910, P50 MH071616, U24 RR021382, R01 MH56584)
- Natural Science Foundation of Jiangsu Province (BK20150982, BK20150983)
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