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Lin W, Tong T, Gao Q, Guo D, Du X, Yang Y, Guo G, Xiao M, Du M, Qu X. Convolutional Neural Networks-Based MRI Image Analysis for the Alzheimer's Disease Prediction From Mild Cognitive Impairment. Front Neurosci 2018; 12:777. [PMID: 30455622 PMCID: PMC6231297 DOI: 10.3389/fnins.2018.00777] [Citation(s) in RCA: 132] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2018] [Accepted: 10/05/2018] [Indexed: 12/18/2022] Open
Abstract
Mild cognitive impairment (MCI) is the prodromal stage of Alzheimer's disease (AD). Identifying MCI subjects who are at high risk of converting to AD is crucial for effective treatments. In this study, a deep learning approach based on convolutional neural networks (CNN), is designed to accurately predict MCI-to-AD conversion with magnetic resonance imaging (MRI) data. First, MRI images are prepared with age-correction and other processing. Second, local patches, which are assembled into 2.5 dimensions, are extracted from these images. Then, the patches from AD and normal controls (NC) are used to train a CNN to identify deep learning features of MCI subjects. After that, structural brain image features are mined with FreeSurfer to assist CNN. Finally, both types of features are fed into an extreme learning machine classifier to predict the AD conversion. The proposed approach is validated on the standardized MRI datasets from the Alzheimer's Disease Neuroimaging Initiative (ADNI) project. This approach achieves an accuracy of 79.9% and an area under the receiver operating characteristic curve (AUC) of 86.1% in leave-one-out cross validations. Compared with other state-of-the-art methods, the proposed one outperforms others with higher accuracy and AUC, while keeping a good balance between the sensitivity and specificity. Results demonstrate great potentials of the proposed CNN-based approach for the prediction of MCI-to-AD conversion with solely MRI data. Age correction and assisted structural brain image features can boost the prediction performance of CNN.
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Affiliation(s)
- Weiming Lin
- College of Physics and Information Engineering, Fuzhou University, Fuzhou, China
- School of Opto-Electronic and Communication Engineering, Xiamen University of Technology, Xiamen, China
- Fujian Key Lab of Medical Instrumentation & Pharmaceutical Technology, Fuzhou, China
| | - Tong Tong
- Fujian Key Lab of Medical Instrumentation & Pharmaceutical Technology, Fuzhou, China
- Imperial Vision Technology, Fuzhou, China
| | - Qinquan Gao
- College of Physics and Information Engineering, Fuzhou University, Fuzhou, China
- Fujian Key Lab of Medical Instrumentation & Pharmaceutical Technology, Fuzhou, China
- Imperial Vision Technology, Fuzhou, China
| | - Di Guo
- School of Computer & Information Engineering, Xiamen University of Technology, Xiamen, China
| | - Xiaofeng Du
- School of Computer & Information Engineering, Xiamen University of Technology, Xiamen, China
| | - Yonggui Yang
- Department of Radiology, Xiamen 2nd Hospital, Xiamen, China
| | - Gang Guo
- Department of Radiology, Xiamen 2nd Hospital, Xiamen, China
| | - Min Xiao
- School of Opto-Electronic and Communication Engineering, Xiamen University of Technology, Xiamen, China
| | - Min Du
- College of Physics and Information Engineering, Fuzhou University, Fuzhou, China
- Fujian Provincial Key Laboratory of Eco-Industrial Green Technology, Nanping, China
| | - Xiaobo Qu
- Department of Electronic Science, Xiamen University, Xiamen, China
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Shelf-Life Prediction of ‘Gros Michel’ Bananas with Different Browning Levels Using Hyperspectral Reflectance Imaging. FOOD ANAL METHOD 2014. [DOI: 10.1007/s12161-014-9960-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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