Early prognosis of Alzheimer's disease based on convolutional neural networks and ensemble learning_Journal of Biomedical Engineering

Authors：

ZENG An ^1,2 , JIA Longfei ¹ ,  PAN Dan ^3,4 , SONG Xiaowei ⁵

1. School of Computers, Guangdong University of Technology, Guangzhou 510006, P.R.China;
2. Guangdong Key Laboratory of Big Data Analysis and Processing, Guangzhou 510006, P.R.China;
3. Modern Educational Technology Center, Guangdong Construction Polytechnic, Guangzhou 510440, P.R.China;
4. Guangzhou Dazhi Networks Technology Co. Ltd., Guangzhou 510000, P.R.China;
5. ImageTech Lab, Simon Fraser University, Vancouver V6B 5K3, Canada;

Corresponding author：

PAN Dan, Email: 2656351065@qq.com

Keywords：

Alzheimer's disease; mild cognitive impairment; convolutional neural networks; ensemble learning; magnetic resonance imaging

DOI：

10.7507/1001-5515.201809040

Video：

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Abstract Full text Figures/Tables Video References Cited by

Alzheimer's disease (AD) is a typical neurodegenerative disease, which is clinically manifested as amnesia, loss of language ability and self-care ability, and so on. So far, the cause of the disease has still been unclear and the course of the disease is irreversible, and there has been no cure for the disease yet. Hence, early prognosis of AD is important for the development of new drugs and measures to slow the progression of the disease. Mild cognitive impairment (MCI) is a state between AD and healthy controls (HC). Studies have shown that patients with MCI are more likely to develop AD than those without MCI. Therefore, accurate screening of MCI patients has become one of the research hotspots of early prognosis of AD. With the rapid development of neuroimaging techniques and deep learning, more and more researchers employ deep learning methods to analyze brain neuroimaging images, such as magnetic resonance imaging (MRI), for early prognosis of AD. Hence, in this paper, a three-dimensional multi-slice classifiers ensemble based on convolutional neural network (CNN) and ensemble learning for early prognosis of AD has been proposed. Compared with the CNN classification model based on a single slice, the proposed classifiers ensemble based on multiple two-dimensional slices from three dimensions could use more effective information contained in MRI to improve classification accuracy and stability in a parallel computing mode.

Citation： ZENG An, JIA Longfei, PAN Dan, SONG Xiaowei. Early prognosis of Alzheimer's disease based on convolutional neural networks and ensemble learning. Journal of Biomedical Engineering, 2019, 36(5): 711-719. doi: 10.7507/1001-5515.201809040 Copy

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1. 吕鸿蒙, 赵地, 迟学斌. 基于增强 AlexNet 的深度学习的阿尔茨海默症的早期诊断. 计算机科学, 2017, 44(S1): 50-60.
2. Prince M, Wimo A, Guerchet M, et al. World Alzheimer Report 2015: The Global Impact of Dementia. London: Alzheimer’s Disease International (ADI), 2015.
3. Liu Siqi, Liu Sidong, Cai Weidong, et al. Early diagnosis of Alzheimer's disease with deep learning// 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI). Beijing: IEEE, 2014: 1015-1018.
4. Tripoliti E E, Fotiadis D I, Argyropoulou M. A supervised method to assist the diagnosis and monitor progression of Alzheimer's disease using data from an fMRI experiment. Artif Intell Med, 2011, 53(1): 35-45.
5. Van Leemput K, Maes F, Vandermeulen D, et al. Automated model-based tissue classification of MR images of the brain. IEEE Trans Med Imaging, 1999, 18(10): 897-908.
6. Hinrichs C, Singh V, Mukherjee L, et al. Spatially augmented LPboosting for AD classification with evaluations on the ADNI dataset. Neuroimage, 2009, 48(1): 138-149.
7. 孙志军, 薛磊, 许阳明, 等. 深度学习研究综述. 计算机应用研究, 2012, 29(8): 2806-2810.
8. Vincent P, Larochelle H, Lajoie I, et al. Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion. J Mach Learn Res, 2010, 11: 3371-3408.
9. Hinton G E. Deep belief networks. Scholarpedia, 2009, 4(5): 5947.
10. Lecun Y, Bottou L, Bengio Y, et al. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 1998, 86(11): 2278-2324.
11. Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks// Advances in neural information processing systems. Lake Tahoe: Curran Associates Inc, 2012: 1097-1105.
12. Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation. IEEE Trans Pattern Anal Mach Intell, 2014, 39(4): 640-651.
13. Girshick R, Donahue J, Darrell T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Columbus: IEEE, 2014: 580-587.
14. Wang S H, Phillips P, Sui Y, et al. Classification of Alzheimer's disease based on eight-layer convolutional neural network with leaky rectified linear unit and max pooling. J Med Syst, 2018, 42(5): 85.
15. 李彦冬, 郝宗波, 雷航. 卷积神经网络研究综述. 计算机应用, 2016, 36(9): 2508-2515, 2565.
16. Breiman L. Bagging predictors. Mach Learn, 1996, 24(2): 123-140.
17. Freund Y, Schapire R E. A decision-theoretic generalization of on-line learning and an application to boosting. J Comput Syst Sci, 1997, 55(1): 119-139.
18. Breman L. Random forest. Mach Learn, 2001, 45(1): 5-32.
19. 张春霞, 张讲社. 选择性集成学习算法综述. 计算机学报, 2011, 34(8): 1399-1410.
20. Salvatore C, Cerasa A, Battista P, et al. Magnetic resonance imaging biomarkers for the early diagnosis of Alzheimer's disease: a machine learning approach. Front Neurosci, 2015, 9: 307.
21. Liew S S, Khalil-Hani M, Bakhteri R. Bounded activation functions for enhanced training stability of deep neural networks on visual pattern recognition problems. Neurocomputing, 2016, 216: 718-734.
22. Kingma D P, Ba J. Adam: A method for stochastic optimization. arXiv preprint arXiv, 2014: 1412. 6980.

Journal of Biomedical Engineering

Early prognosis of Alzheimer's disease based on convolutional neural networks and ensemble learning

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