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Current Medical Imaging


ISSN (Print): 1573-4056
ISSN (Online): 1875-6603

Research Article

Convolutional Neural Network-based MR Image Analysis for Alzheimer’s Disease Classification

Author(s): Boo-Kyeong Choi, Nuwan Madusanka, Heung-Kook Choi*, Jae-Hong So, Cho-Hee Kim, Hyeon-Gyun Park, Subrata Bhattacharjee and Deekshitha Prakash

Volume 16, Issue 1, 2020

Page: [27 - 35] Pages: 9

DOI: 10.2174/1573405615666191021123854

Price: $65


Background: In this study, we used a convolutional neural network (CNN) to classify Alzheimer’s disease (AD), mild cognitive impairment (MCI), and normal control (NC) subjects based on images of the hippocampus region extracted from magnetic resonance (MR) images of the brain.

Methods: The datasets used in this study were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI). To segment the hippocampal region automatically, the patient brain MR images were matched to the International Consortium for Brain Mapping template (ICBM) using 3D-Slicer software. Using prior knowledge and anatomical annotation label information, the hippocampal region was automatically extracted from the brain MR images.

Results: The area of the hippocampus in each image was preprocessed using local entropy minimization with a bi-cubic spline model (LEMS) by an inhomogeneity intensity correction method. To train the CNN model, we separated the dataset into three groups, namely AD/NC, AD/MCI, and MCI/NC. The prediction model achieved an accuracy of 92.3% for AD/NC, 85.6% for AD/MCI, and 78.1% for MCI/NC.

Conclusion: The results of this study were compared to those of previous studies, and summarized and analyzed to facilitate more flexible analyses based on additional experiments. The classification accuracy obtained by the proposed method is highly accurate. These findings suggest that this approach is efficient and may be a promising strategy to obtain good AD, MCI and NC classification performance using small patch images of hippocampus instead of whole slide images.

Keywords: Convolution neural network, Alzheimer’s diseases, mild cognitive impairments, normal controls, hippocampus, local entropy.

Graphical Abstract
Choi BK. Diagnosis and classification of Alzheimer’s patients using Convolution Neural Network Master Thesis, Inje University Graduate School:Gimhae February 2019.
Jeong KH. Artificial intelligence based medical image analysis technology trend. Korea Information and Communication Technology Promotion Center 2018; pp. 1-12.
Lee YH, Kim HJ, Kim GB, Kim NK. Deep learning-based feature extraction for medical image analysis. Korean Society of Imaging Informatics in Medicine 2014; 20: 1-12.
Kim KW, Jo HS, Jo YH. Korea 2017 central dementia center annual report Korea Central Dementia Center 2018; 1-63.
Choi BK, So JH, Son YJ, et al. Dementia classification by distance analysis from the central coronal plane of the brain hippocampus. J Korea Multimed Soc 2018; 21(2): 147-57.
Madusanka N, Choi HK, So JH, et al. Alzheimer’s disease classification based on multi-feature fusion. Curr Med Imaging Rev 2018; 15(2): 161-9.
Choi BK, So JH, Madusanka N, et al. Classification and diagnosis of Alzheimer’s disease using Convolution Neural Network. Japan-Korea Joint Workshop on Complex Communication Sciences. 1-4.
Pieper S, Halle M, Kikinis R. 3D Slicer. 2nd IEEE International Symposium on Biomedical Imaging: Nano to Macro. 632-5.
Hou H, Andrews H. Cubic splines for image interpolation and digital filtering. IEEE Trans Acoust 1978; 26(6): 508-17.
Salvado O, Hillenbrand C, Zhang S, Wilson DL. Method to correct intensity inhomogeneity in MR images for atherosclerosis characterization. IEEE Trans Med Imaging 2006; 25(5): 539-52.
[] [PMID: 16689259]
Uwano I, Kudo K, Yamashita F, et al. Intensity inhomogeneity correction for magnetic resonance imaging of human brain at 7T. Med Phys 2014; 41(2)022302
[] [PMID: 24506640]
Anthimopoulos M, Christodoulidis S, Ebner L, Christe A, Mougiakakou S. Lung pattern classification for interstitial lung diseases using a deep convolutional neural network. IEEE Trans Med Imaging 2016; 35(5): 1207-16.
[] [PMID: 26955021]
LeCun Y, Boser B, Denker JS, et al. Backpropagation applied to handwritten zip code recognition. AT&T Bell Laboratories Holmdel 1989; pp. 1-11.
Behnke S. Hierarchical neural networks for image interpretation. Berlin: Springer 2003; pp. 135-40.
Simard PY, Steinkraus D, Platt JC. Best practices convolutional neural networks applied to visual document analysis. Microsoft Research, One Microsoft Way 2003; pp. 1-6.
Deng X, Liu Q, Deng Y, et al. An improved method to construct basic probability assignment based on the confusion matrix for classification problem. Inf Sci 2016; 340: 250-61.
Wei Q, Wang L, Wang Q, Kruger WD, Dunbrack RL Jr. Testing computational prediction of missense mutation phenotypes: functional characterization of 204 mutations of human cystathionine beta synthase. Proteins 2010; 78(9): 2058-74.
[] [PMID: 20455263]
Liu M, Cheng D, Yan W. Classification of Alzheimer’s disease by combination of convolutional and recurrent neural networks using FDG-PET images. Front Neuroinform 2018; 12: 35.
[] [PMID: 29970996]
Hosseini-Asl E, Keynto R, El-Baz A. Alzheimer's disease diagnostics by adaptation of 3D Convolutional Network. IEEE ICIP 2016 Conference: 2016; 1-5.
Li R, Zhang W, Suk HI, et al. Deep learning based imaging data completion for improved brain disease diagnosis.Med Image Comput Comput Assist Interv. 2014; 17.(Pt 3): 305-12.
[] [PMID: 25320813]
Payan A, Montana G. Predicting Alzheimer’s disease: a neuroimaging study with 3D convolutional neural networks. Cornell University Libary 2015; pp. 1-9.
Suk HI, Shen D. Deep learning-based feature representation for ad/mci classification. Proceedings of the Medical Image Computing and Computer-Assisted Intervention–MICCAI. 583-90.
Suk HI, Lee SW, Shen D. Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis. Neuroimage 2014; 101: 569-82.
[] [PMID: 25042445]
Zhu X, Suk HI, Shen D. A novel matrix-similarity based loss function for joint regression and classification in AD diagnosis. Neuroimage 2014; 100: 91-105.
[] [PMID: 24911377]
Zu C, Jie B, Liu M, et al. Label-aligned multi-task feature learning for multimodal classification of Alzheimers disease and mild cognitive impairment. Brain Imaging Behav 2015; 1-12.
Liu S, Liu S, Cai W, et al. Multimodal neuroimaging feature learning for multiclass diagnosis of Alzheimer’s disease. IEEE Trans Biomed Eng 2015; 62(4): 1132-40.
[] [PMID: 25423647]
Liu M, Zhang D, Adeli E, Shen D. Inherent structure-based multiview learning with multitemplate feature representation for Alzheimer’s disease diagnosis. IEEE Trans Biomed Eng 2016; 63(7): 1473-82.
[] [PMID: 26540666]
Sarraf S, Tofighi G. Classification of Alzheimer’s disease using fMRI data and deep learning convolutional neural networks. Cornell University Libary 2016; pp. 1-5.
So JH, Son YJ, Choi BK, et al. A software design for 2D and 3D texture analysis. J Korea Multimed Soc 2016; 19(1): 740-3.
Xiao Z, Ding Y, Lan T, Zhang C, Luo C, Qin Z. Brain MR image classification for Alzheimer’s disease diagnosis based on multifeature fusion. Comput Math Methods Med 2017; 2017: 1-13.
[] [PMID: 28611848]
Kimura J, Shibasaki H. Recent advances in clinical neurophysiology. Proceedings of the 10th International Congress of EMG and Clinical Neurophysiology. 15-9.
Beheshti I, Demirel H, Matsuda H. Classification of Alzheimer’s disease and prediction of mild cognitive impairment-to-Alzheimer’s conversion from structural magnetic resource imaging using feature ranking and a genetic algorithm. Comput Biol Med 2017; 83: 109-19.
Park GW. Introduction and case presentation - typical case of mild cognitive impairment. Korean Neuroscience Association 2017; pp. 71-7.

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