Abstract
In recent years, interest has grown in using computer-aided diagnosis (CAD) for Alzheimer's disease (AD) and its prodromal stage, mild cognitive impairment (MCI). However, existing CAD technologies often overfit data and have poor generalizability. In this study, we proposed a sparse-response deep belief network (SR-DBN) model based on rate distortion (RD) theory and an extreme learning machine (ELM) model to distinguish AD, MCI, and normal controls (NC). We used [18F]-AV45 positron emission computed tomography (PET) and magnetic resonance imaging (MRI) images from 340 subjects enrolled in the ADNI database, including 116 AD, 82 MCI, and 142 NC subjects. The model was evaluated using five-fold cross-validation. In the whole model, fast principal component analysis (PCA) served as a dimension reduction algorithm. An SR-DBN extracted features from the images, and an ELM obtained the classification. Furthermore, to evaluate the effectiveness of our method, we performed comparative trials. In contrast experiment 1, the ELM was replaced by a support vector machine (SVM). Contrast experiment 2 adopted DBN without sparsity. Contrast experiment 3 consisted of fast PCA and an ELM. Contrast experiment 4 used a classic convolutional neural network (CNN) to classify AD. Accuracy, sensitivity, specificity, and area under the curve (AUC) were examined to validate the results. Our model achieved 91.68% accuracy, 95.47% sensitivity, 86.68% specificity, and an AUC of 0.87 separating between AD and NC groups; 87.25% accuracy, 79.74% sensitivity, 91.58% specificity, and an AUC of 0.79 separating MCI and NC groups; and 80.35% accuracy, 85.65% sensitivity, 72.98% specificity, and an AUC of 0.71 separating AD and MCI groups, which gave better classification than other models assessed.
Highlights
Alzheimer’s disease (AD) is a neurodegenerative disease characterized by cognitive dysfunction and associated with advanced age
In the classification of AD and normal controls (NC), our model achieved 91.68% accuracy, 95.47% sensitivity, 86.68% specificity, and an area under the curve (AUC) of 0.87
In the classification between mild cognitive impairment (MCI) and NC, the model achieved 87.25% accuracy, 79.74% sensitivity, 91.58% specificity, and an AUC of 0.79
Summary
Alzheimer’s disease (AD) is a neurodegenerative disease characterized by cognitive dysfunction and associated with advanced age. Because there are currently no therapies that can reverse the course of AD, it is important to diagnose AD and its prodromal stage, mild cognitive impairment (MCI) as early as possible [1]. In recent years, neuroimaging techniques have been shown to be effective tools for the diagnosis of AD. Magnetic resonance imaging (MRI) and positron emission tomography (PET) are two common neuroimaging methods. Deep learning methods have shown great promise for image analysis and disease prediction. Liu et al designed a deep learning architecture to more accurately differentiate AD, MCI, and normal controls (NC). A few of deep learning studies based on PET/MRI could be observed [6, 7]
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.