Assisted Diagnosis of Alzheimer’s Disease Based on Deep Learning and Multimodal Feature Fusion

With the development of artificial intelligence technologies, it is possible to use computer to read digital medical images. Because Alzheimer’s disease (AD) has the characteristics of high incidence and high disability, it has attracted the attention of many scholars, and its diagnosis and treatment have gradually become a hot topic. In this paper, a multimodal diagnosis method for AD based on three-dimensional shufflenet (3DShuffleNet) and principal component analysis network (PCANet) is proposed. First, the data on structural magnetic resonance imaging (sMRI) and functional magnetic resonance imaging (fMRI) are preprocessed to remove the influence resulting from the differences in image size and shape of different individuals, head movement, noise, and so on. Then, the original two-dimensional (2D) ShuffleNet is developed three-dimensional (3D), which is more suitable for 3D sMRI data to extract the features. In addition, the PCANet network is applied to the brain function connection analysis, and the features on fMRI data are obtained. Next, kernel canonical correlation analysis (KCCA) is used to fuse the features coming from sMRI and fMRI, respectively. Finally, a good classification effect is obtained through the support vector machines (SVM) method classifier, which proves the feasibility and effectiveness of the proposed method.

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Applying 3DPCANet and Functional Magnetic Resonance Imaging to Aided Diagnosis of Alzheimer’s Disease

10.21203/rs.3.rs-820356/v1 ◽

2021 ◽

Author(s):

Yu Wang ◽

Hongfei Jia ◽

Yifan Duan ◽

Hongbing Xiao

Keyword(s):

Magnetic Resonance Imaging ◽

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Magnetic Resonance ◽

Functional Magnetic Resonance Imaging ◽

Brain Regions ◽

Support Vector ◽

Functional Magnetic Resonance ◽

Resonance Imaging ◽

Public Data

Abstract Alzheimer's disease (AD) is a progressive neurodegenerative disease, which changes the structure of brain regions by some hidden causes. In this paper for assisting doctors to make correct judgments, an improved 3DPCANet method is proposed to classify AD by combining the mean (mALFF) of the whole brain. The main idea includes that firstly, the functional magnetic resonance imaging (fMRI) data is pre-processed, and mALFF is calculated to get the corresponding matrix. Then the features of mALFF images are extracted via the improved 3DPCANet network. Finally, AD patients with different stages are classified using support vector machine (SVM). Experiments results based on public data from the Alzheimer’s disease neuroimaging initiative (ADNI) show that the proposed approach has better performance compared with state-of-the-art methods. The accuracies of AD vs. significant memory concern (SMC), SMC vs. late mild cognitive impairment (LMCI), and normal control (NC) vs. SMC reach respectively 92.42%, 91.80%, and 89.50%, which testifies the feasibility and effectiveness of the proposed method.

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Detection of Alzheimer’s Disease by Three-Dimensional Displacement Field Estimation in Structural Magnetic Resonance Imaging

Journal of Alzheimer s Disease ◽

10.3233/jad-150848 ◽

2015 ◽

Vol 50 (1) ◽

pp. 233-248 ◽

Cited By ~ 33

Author(s):

Shuihua Wang ◽

Yudong Zhang ◽

Ge Liu ◽

Preetha Phillips ◽

Ti-Fei Yuan

Keyword(s):

Magnetic Resonance Imaging ◽

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Magnetic Resonance ◽

Displacement Field ◽

Three Dimensional ◽

Resonance Imaging ◽

Structural Magnetic Resonance Imaging ◽

Field Estimation

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Classification of Alzheimer’s Disease Stages: An Approach Using PCA-Based Algorithm

American Journal of Alzheimer s Disease & Other Dementias® ◽

10.1177/1533317518790038 ◽

2018 ◽

Vol 33 (7) ◽

pp. 433-439 ◽

Cited By ~ 1

Author(s):

Fayyaz Ahmad ◽

Waqar Mahmood Dar

Keyword(s):

Magnetic Resonance Imaging ◽

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Face Recognition ◽

Magnetic Resonance ◽

Principal Component ◽

Recognition Algorithm ◽

Fmri Data ◽

Resonance Imaging

Early diagnosis of Alzheimer’s disease (AD) allows individuals and their health managers to manage healthier medication. We proposed an approach for classification of AD stages, with respect to principal component analysis (PCA)-based algorithm. The PCA has been extensively applied as the most auspicious face-recognition algorithm. For the proposed algorithm, 100 images of 10 children were transformed for feature extraction and covariance matrix was constructed to obtain eigenvalues. The eigenvector provided a useful framework for face recognition. For the classification of AD stages, magnetic resonance imaging (MRI) and functional magnetic resonance imaging (fMRI) data were obtained from Alzheimer’s Disease Neuroimaging Initiative database. Hippocampus is one of the most affected regions by AD. Thus, we selected clusters of voxels from the “hippocampus” of AD screening stage (mild cognitive impairment), AD stage 1, AD stage 2, and AD stage 3. By using eigenvectors corresponding to maximum eigenvalues of fMRI data, the purposed algorithm classified the voxels of AD stages effectively.

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A Comprehensive Machine-Learning Model Applied to Magnetic Resonance Imaging (MRI) to Predict Alzheimer’s Disease (AD) in Older Subjects

Journal of Clinical Medicine ◽

10.3390/jcm9072146 ◽

2020 ◽

Vol 9 (7) ◽

pp. 2146

Author(s):

Gopi Battineni ◽

Nalini Chintalapudi ◽

Francesco Amenta ◽

Enea Traini

Keyword(s):

Magnetic Resonance Imaging ◽

Machine Learning ◽

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Feature Selection ◽

Magnetic Resonance ◽

Neurodegenerative Disorder ◽

Support Vector ◽

Resonance Imaging ◽

Magnetic Resonance Imaging Mri

Increasing evidence suggests the utility of magnetic resonance imaging (MRI) as an important technique for the diagnosis of Alzheimer’s disease (AD) and for predicting the onset of this neurodegenerative disorder. In this study, we present a sophisticated machine learning (ML) model of great accuracy to diagnose the early stages of AD. A total of 373 MRI tests belonging to 150 subjects (age ≥ 60) were examined and analyzed in parallel with fourteen distinct features related to standard AD diagnosis. Four ML models, such as naive Bayes (NB), artificial neural networks (ANN), K-nearest neighbor (KNN), and support-vector machines (SVM), and the receiver operating characteristic (ROC) curve metric were used to validate the model performance. Each model evaluation was done in three independent experiments. In the first experiment, a manual feature selection was used for model training, and ANN generated the highest accuracy in terms of ROC (0.812). In the second experiment, automatic feature selection was conducted by wrapping methods, and the NB achieved the highest ROC of 0.942. The last experiment consisted of an ensemble or hybrid modeling developed to combine the four models. This approach resulted in an improved accuracy ROC of 0.991. We conclude that the involvement of ensemble modeling, coupled with selective features, can predict with better accuracy the development of AD at an early stage.

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