Early Diagnosis of Alzheimer’s Disease Using Cerebral Catheter Angiogram Neuroimaging: A Novel Model Based on Deep Learning Approaches
Abstract
:1. Introduction
2. Background and Literature Review
2.1. Background
2.2. Literature Review
2.2.1. Classical Machine Learning-Based Methods
2.2.2. Deep Learning-Based Methods
3. Methodology
3.1. Dataset Used
3.2. Preprocessing and Augmentation
3.3. Proposed Framework
- 1.
- InceptionV3: The third version of GoogleNet, a CNN architecture released in 2015 by Google [68]. It has won the ILSVRC championship in 2015 and improving the Top-1 performance by 15% using 92 MB of parameters. InceptionV3 uses 48 layers of neural networks with 23,851,784 parameters and 159 depth size. The network has an image input size of 299 × 299, and it has learned rich feature representations for a wide range of images. Figure 6 shows the main architecture of the InceptionV3 model.
- 2.
- DenseNet201: The third version of densely connected convolutional networks (DenseNet), a CNN architecture released in 2017 CVPR and jointly invented by Cornwell University, Tsinghua University, and Facebook AI Research (FAIR) [69,77]. It is improving the Top-1 performance by 77.3% using 80 MB of parameters. DenseNet-201 uses 201 layers of neural networks with 20,242,984 parameters. The network has an image input size of 224 × 224 and has learned rich feature representations for a wide range of images. Figure 7 shows the main architecture of the DenseNet201 model.
4. Experimental Results
4.1. Experiments
4.2. Results
4.3. Comparison with Previous Works
5. Conclusions and Future Work
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Neuroimaging | Uses | Advantages | Disadvantages |
---|---|---|---|
CT | Determine brain atrophy | Short time study and high quality | Requires large radiation doses |
SPECT | Determine Beta-amyloid deposition and neurofibrillary tangles | Well-supplied and has a low cost | Not able to differentiate between Alzheimer’s and other Dementia diseases |
MRI | Analyze vital signs of neuronal loss | Distinguish between Alzheimer’s disease and other Dementia diseases | Very expensive and time-consuming |
MRA | Evaluate age-related changes in the cerebral arteries | Detect dementia diseases | Difficult to evaluate small vessels |
PET | Reveal tissues and organs functions | Evaluate brain amyloid | Erroneous interpretations |
Database | PET | SPECT | MRI | CT |
---|---|---|---|---|
ADNI [37] | Yes | No | Yes | No |
HMSD [38] | Yes | Yes | Yes | Yes |
OASIS [39] | No | No | Yes | No |
Diagnostic Type | Scans No. | Age [Range] | Gender (M/F) |
---|---|---|---|
Normal Controls | 27 | 47.9 ± 14.8 [8–66] | 15/12 |
Alzheimer’s disease | 26 | 56.8 ± 7.3 [42–81] | 14/12 |
Predicted Values | |||
---|---|---|---|
Negative | Positive | ||
Actual values | Negative | TN | FP |
Positive | FN | TP |
Features Extractor | Classifier | ACC. | PREC. | REC. | F1 |
---|---|---|---|---|---|
DenseNet201 | SVM | 98.57 ± 0.23 | 98.75 ± 0.37 | 98.44 ± 0.32 | 98.52 ± 0.24 |
LR | 99.14 ± 0.18 | 99.98 ± 0.01 | 98.00 ± 0.42 | 98.94 ± 0.22 | |
LDA | 71.43 ± 0.59 | 67.91 ± 0.81 | 82.52 ± 1.02 | 74.14 ± 0.73 | |
SGD | 94.29 ± 0.38 | 99.98 ± 0.01 | 96.89 ± 0.52 | 97.10 ± 0.33 | |
InceptionV3 | SVM | 98.29 ± 0.22 | 99.98 ± 0.01 | 96.47 ± 0.46 | 98.14 ± 0.24 |
LR | 98.00 ± 0.22 | 99.98 ± 0.01 | 96.03 ± 0.45 | 97.92 ± 0.23 | |
LDA | 70.29 ± 0.69 | 65.85 ± 0.71 | 86.46 ± 0.10 | 74.32 ± 0.67 | |
SGD | 96.00 ± 0.36 | 99.98 ± 0.01 | 95.34 ± 0.33 | 95.02 ± 0.49 | |
InceptionV3 + DenseNet201 | SVM | 98.86 ± 0.19 | 99.98 ± 0.01 | 97.74 ± 0.43 | 98.67 ± 0.22 |
LR | 99.14 ± 0.18 | 99.98 ± 0.01 | 98.44 ± 0.32 | 99.19 ± 0.17 | |
LDA | 67.71 ± 0.66 | 64.20 ± 0.99 | 83.30 ± 0.97 | 72.01 ± 0.81 | |
SGD | 97.71 ± 0.21 | 99.98 ± 0.01 | 95.22 ± 0.52 | 97.96 ± 0.33 |
Study | Modality | Feature Extraction Method | Classifier | AD | NC | ACC. |
---|---|---|---|---|---|---|
Lebedev et al. [52] | Structural MRI | Surface-based registration | Random Forest | 185 | 225 | 90.30 |
Zhang and Wang [53] | 3D-MRI | Displacement Field | Twin SVM | 28 | 98 | 92.70 |
Beheshti et al. [54] | Structural MRI | Voxel-based feature extraction | SVM | 130 | 130 | 92.40 |
Zhang et al. [55] | Structural MRI | Bag-of-words | SVM | 154 | 207 | 88.30 |
Zeng et al. [56] | MRI | Anatomical Labeling | SDPSO-SVM-PCA | 92 | 82 | 71.20 |
Koh et al. [57] | MRI | BE Mode Decomposition | SVM-Poly-1 | 55 | 110 | 93.90 |
Liu et al. [60] | 3D-FDG-PET | 2D-CNN and BGRU | Softmax | 93 | 100 | 91.20 |
Ge et al. [61] | 3D-MRI | 3D-mutliscale-CNN | XGBoost | 198 | 139 | 98.20 |
Basaia et al. [6] | MRI | CNN | LR | 542 | 457 | 98.00 |
Pan et al. [64] | MRI | 2D-CNN | Ensemble | 137 | 162 | 84.00 |
Feng et al. [65] | 3D-MRI | 3D-CNN | SVM | 153 | 159 | 99.10 |
Li et al. [66] | 4D-MRI | 3D-CNN and LSTM | Softmax | 116 | 174 | 97.30 |
Liu et al. [67] | MRI | GoogleNet | Softmax | 30 | 332 | 93.00 |
Proposed Research | DSA | InceptionV3 + DenseNet201 | LR | 13 | 27 | 99.14 |
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Gharaibeh, M.; Almahmoud, M.; Ali, M.Z.; Al-Badarneh, A.; El-Heis, M.; Abualigah, L.; Altalhi, M.; Alaiad, A.; Gandomi, A.H. Early Diagnosis of Alzheimer’s Disease Using Cerebral Catheter Angiogram Neuroimaging: A Novel Model Based on Deep Learning Approaches. Big Data Cogn. Comput. 2022, 6, 2. https://doi.org/10.3390/bdcc6010002
Gharaibeh M, Almahmoud M, Ali MZ, Al-Badarneh A, El-Heis M, Abualigah L, Altalhi M, Alaiad A, Gandomi AH. Early Diagnosis of Alzheimer’s Disease Using Cerebral Catheter Angiogram Neuroimaging: A Novel Model Based on Deep Learning Approaches. Big Data and Cognitive Computing. 2022; 6(1):2. https://doi.org/10.3390/bdcc6010002
Chicago/Turabian StyleGharaibeh, Maha, Mothanna Almahmoud, Mostafa Z. Ali, Amer Al-Badarneh, Mwaffaq El-Heis, Laith Abualigah, Maryam Altalhi, Ahmad Alaiad, and Amir H. Gandomi. 2022. "Early Diagnosis of Alzheimer’s Disease Using Cerebral Catheter Angiogram Neuroimaging: A Novel Model Based on Deep Learning Approaches" Big Data and Cognitive Computing 6, no. 1: 2. https://doi.org/10.3390/bdcc6010002