Early diagnosis of Alzheimer's disease based on three-dimensional convolutional neural networks ensemble model combined with genetic algorithm_Journal of Biomedical Engineering

Authors：

PAN Dan ¹ , ZOU Chao ² , RONG Huabin ² ,  ZENG An ²

1. School of Electronics and Information, Guangdong Polytechnic Normal University, Guangzhou 510665, P.R.China;
2. Faculty of Computer, Guangdong University of Technology, Guangzhou 510006, P.R.China;

Corresponding?author：

ZENG An, Email: 437871335@qq.com

Keywords：

Alzheimer's disease; classification; convolutional neural network; genetic algorithm; region of interest

DOI：

10.7507/1001-5515.201911046

Video：

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The pathogenesis of Alzheimer's disease (AD), a common neurodegenerative disease, is still unknown. It is difficult to determine the atrophy areas, especially for patients with mild cognitive impairment (MCI) at different stages of AD, which results in a low diagnostic rate. Therefore, an early diagnosis model of AD based on 3-dimensional convolutional neural network (3DCNN) and genetic algorithm (GA) was proposed. Firstly, the 3DCNN was used to train a base classifier for each region of interest (ROI). And then, the optimal combination of the base classifiers was determined with the GA. Finally, the ensemble consisting of the chosen base classifiers was employed to make a diagnosis for a patient and the brain regions with significant classification capability were decided. The experimental results showed that the classification accuracy was 88.6% for AD vs. normal control (NC), 88.1% for MCI patients who will convert to AD (MCIc) vs. NC, and 71.3% for MCI patients who will not convert to AD (MCInc) vs. MCIc. In addition, with the statistical analysis of the behavioral domains corresponding to ROIs (i.e. brain regions), besides left hippocampus, medial and lateral amygdala, and left para-hippocampal gyrus, anterior superior temporal sulcus of middle temporal gyrus and dorsal area 23 of cingulate gyrus were also found with GA. It is concluded that the functions of the selected brain regions mainly are relevant to emotions, memory, cognition and the like, which is basically consistent with the symptoms of indifference, memory losses, mobility decreases and cognitive declines in AD patients. All of these show that the proposed method is effective.

Citation： PAN Dan, ZOU Chao, RONG Huabin, ZENG An. Early diagnosis of Alzheimer's disease based on three-dimensional convolutional neural networks ensemble model combined with genetic algorithm. Journal of Biomedical Engineering, 2021, 38(1): 47-55, 64. doi: 10.7507/1001-5515.201911046 Copy

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27.	Xia Mingrui, Wang Jinhui, He Yong. BrainNet Viewer: a network visualization tool for human brain connectomics. PloS One, 2013, 8(7): e68910.
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29.	Ulep M G, Saraon S K, McLea S. Alzheimer disease. JNP-J Nurse Pract, 2018, 14(3): 129-135.

1. Silveira M, Marques J. Boosting Alzheimer diseasediagnosis using PET images// 2010 20th International Conference on Pattern Recognition (ICPR). Cancun: IEEE, 2010: 2556–2559.
2. 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.
3. Patterson C. World Alzheimer Report 2018 – state of the art of dementia research: New frontiers. London: Alzheimer's Disease International (ADI), 2018.
4. 滕羽鷗, 時晶, 曹天雨. 丁苯酞軟膠囊治療阿爾茨海默病的有效性和安全性的Meta分析. 中國循證醫學雜志, 2019, 19(12): 1446-1452.
5. Jack C R Jr, Albert M S, Knopman D S, et al. Introduction to the recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimers Dement, 2011, 7(3): 257-262.
6. Liu Feng, Wee C Y, Chen Huafu, et al. Inter-modality relationship constrained multi-modality multi-task feature selection for Alzheimer's disease and mild cognitive impairment identification. Neuroimage, 2014, 84: 466-475.
7. Bron E E, Smits M, Van Der Filer W M, et al. Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: the CADDementia challenge. Neuroimage, 2015, 111: 562-579.
8. 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.
9. Feng Chiyu, Elazab A, Yang Peng, et al. Deep learning framework for Alzheimer's disease diagnosis via 3D-CNN and FSBi-LSTM. IEEE Access, 2019, 7: 63605-63618.
10. Tong T, Wolz R, Gao Q, et al. Multiple instance learning for classification of dementia in brain MRI. Med Image Anal, 2014, 18(5): 808-818.
11. Hosseini-Asl E, Keynton R, El-Baz A. Alzheimer's disease diagnostics by adaptation of 3D convolutional network// 2016 IEEE International Conference on Image Processing (ICIP). Phoenix: IEEE, 2016: 126-130.
12. Li Fan, Liu Manhua, Alzheimer’s Disease Neuroimaging Initiative, et al. Alzheimer’s disease diagnosis based on multiple cluster dense convolutional networks. Comput Med Imaging Graph, 2018, 70: 101-110.
13. Huang Gao, Liu Zhuang, Van Der Maaten L, et al. Densely connected convolutional networks// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Hawaii: IEEE, 2017: 4700-4708.
14. 曾安, 賈龍飛, 潘丹, 等. 基于卷積神經網絡和集成學習的阿爾茨海默癥早期診斷. 生物醫學工程學雜志, 2019, 36(5): 711-719.
15. Wang ShuiHua, Phillips P, Sui Yuxiu, et al. Classification of Alzheimer's disease based on eight-layer convolutional neural network with leaky rectified linear unit and max pooling. Comput Med Imaging Graph, 2018, 42(5): 85.
16. Spasov S, Passamonti L, Duggento A, et al. A parameter-efficient deep learning approach to predict conversion from mild cognitive impairment to Alzheimer's disease. NeuroImage, 2019, 189: 276-287.
17. Gupta A, Maida A, Ayhan M. Natural image bases to represent neuroimaging data// International Conference on Machine Learning (ICML). Atlanta: IMLS, 2013: 987-994.
18. Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition// Proceedings of the Internation Conference on Learning Representations (ICLR). San Diego: ICLR, 2015: 1-14.
19. Korolev S, Safiullin A, Belyaev M, et al. Residual and plain convolutional neural networks for 3D brain MRI classification// IEEE International Symposium on Biomedical Imaging (ISBL). Melbourne: IEEE, 2017: 835-838.
20. Young J M. Probabilistic prediction of Alzheimer’s disease from multimodal image data with Gaussian process. London: University of London, 2015.
21. Fan Lingzhong, Li Hai, Zhuo Junjie, et al. The human brainnetome atlas: a new brain atlas based on connectional architecture. Cereb Cortex, 2016, 26(8): 3508-3526.
22. Glorot X, Bengio Y. Understanding the difficulty of training deep feedforward neural network. J Mach Learn Res, 2010, 9: 249-256.
23. Kingma D P, Ba J L. Adam: A method for stochastic optimization. arXiv preprint arXiv, 2014: 1412.6980.
24. Yang Wenlu, Lui R L M, Gao Jiahong, et al. Independent component analysis-based classification of Alzheimer's disease MRI data. J Alzheimers Dis, 2011, 24(4): 775-783.
25. Batmanghelich N, Taskar B, Davatzikos C. A general and unifying framework for feature construction, in image-based pattern classification// International Conference on Information Processing in Medical Imaging (IPMI). Berlin: Springer, 2009: 423-434.
26. 李書通, 肖斌, 李偉生, 等. 基于3D-PCANet的阿爾茲海默病輔助診斷. 計算機科學, 2018(S1): 140-142, 156.
27. Xia Mingrui, Wang Jinhui, He Yong. BrainNet Viewer: a network visualization tool for human brain connectomics. PloS One, 2013, 8(7): e68910.
28. Aderghal K, Benois-Pineau J, Afdel K, et al. FuseMe: Classification of sMRI images by fusion of Deep CNNs in 2D+? projections// International Workshop on Content-based Multimedia Indexing (CBMI). Firenze: ACM, 2017: 1-7.
29. Ulep M G, Saraon S K, McLea S. Alzheimer disease. JNP-J Nurse Pract, 2018, 14(3): 129-135.

Journal of Biomedical Engineering

Early diagnosis of Alzheimer's disease based on three-dimensional convolutional neural networks ensemble model combined with genetic algorithm

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