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.
Highlights
Alzheimer’s disease (AD) is the cause of over 60% of dementia cases (Burns and Iliffe, 2009), in which patients usually have a progressive loss of memory, language disorders and disorientation
Redundant features maybe exist among convolutional neural networks (CNN)-based features, we introduced the principle component analysis (PCA) (Avci and Turkoglu, 2009; Babaoglu et al, 2010; Wu et al, 2013) and least absolute shrinkage and selection operator (LASSO) (Kukreja et al, 2006; Usai et al, 2009; Yamada et al, 2014) to reduce the final number of features
We compared the performance in four different conditions: (1) The CNN was trained with AD/normal controls (NC) patches and used to classify AD/NC subjects; (2) The CNN was trained with converters/non-converters patches and used to classify converters/non-converters; (3) The CNN was
Summary
Alzheimer’s disease (AD) is the cause of over 60% of dementia cases (Burns and Iliffe, 2009), in which patients usually have a progressive loss of memory, language disorders and disorientation. Many previous studies used neuroimaging biomarkers to classify AD patients at different disease stages or to predict the MCI-to-AD conversion (Cuingnet et al, 2011; Zhang et al, 2011; Tong et al, 2013, 2017; Guerrero et al, 2014; Suk et al, 2014; Cheng et al, 2015; Eskildsen et al, 2015; Li et al, 2015; Liu et al, 2015; Moradi et al, 2015) In these studies, structural magnetic resonance imaging (MRI) is one of the most extensively utilized imaging modality due to non-invasion, high resolution and moderate cost
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