MCI Conversion Prediction Using 3D Zernike Moments and the Improved Dynamic Particle Swarm Optimization Algorithm
Abstract
:1. Introduction
2. Material and Methods
2.1. Samples and Image Preprocessing
2.2. Methodology of the Proposed Prediction Model
2.2.1. Feature Extraction Based on 3D-ZM
2.2.2. Feature Selection Based on IDPSO
2.2.3. Classifier
2.3. Alternative Methods
3. Results
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Category | Gender (F/M) | Age | CDR | MMSE |
---|---|---|---|---|
pMCI | 39/66 | 74.95 ± 6.6 | 0.5 ± 0.0 | 26.6 ± 1.7 |
sMCI | 41/80 | 74.45 ± 7.58 | 0.5 ± 0.0 | 27.3 ± 1.87 |
No. Feature | f1 | f2 | f3 | f4 | f5 | … | fn−1 | fn |
---|---|---|---|---|---|---|---|---|
Particle value | 1 | 0 | 1 | 1 | 0 | … | 1 | 0 |
Method | Accuracy (%) | Sensitivity (%) | Specificity (%) | AUC |
---|---|---|---|---|
Voxel-based | 50.88 | 46.67 | 54.55 | 0.51 |
3DZM-based | 53.10 | 50.48 | 55.37 | 0.523 |
PSO-3DZM-based | 68.58 | 71.43 | 66.12 | 0.69 |
IDPSO-3DZM-based | 75.66 | 83.81 | 68.60 | 0.76 |
Reference | pMCI/sMCI | Validation Method | ACC (%) | SEN (%) | SPE (%) | AUC | Conversion Time (Months) |
---|---|---|---|---|---|---|---|
Xiao et al. (2020) [50] | 51/45 | 10-Fold | 75.87 | 73.71 | 77.51 | --- | --- |
Abrol et al. (2020) [51] | 189/245 | 5-Fold | 83.01 | --- | ---- | --- | 0–36 |
Beheshti et al. (2017) [47] | 71/62 | 10-Fold | 75.00 | 76.92 | 73.23 | 0.75 | 0–36 |
Lin et al. (2021) [48] | 110/208 | 5-Fold | 81.2% | --- | ---- | --- | --- |
Liu et al. (2013) [52] | 97/93 | --- | 69.00 | 66.00 | 72.00 | --- | 0–36 |
Beheshti et al. (2019) [14] | 112/102 | 10-Fold | 74.77 | 73.21 | 76.47 | 0.74 | 0–36 |
Liu et al. (2018) [53] | 120/160 | 10-Fold | 72.08 | 75:11 | 71.05 | 0.71 | 0–18 |
Proposed Method | 105/121 | 10-Fold | 75.66 | 83.81 | 68.60 | 0.75 | 0–36 |
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Bolourchi, P.; Gholami, M.; Moradi, M.; Beheshti, I.; Demirel, H., on behalf of the Alzheimer’s Disease Neuroimaging Initiative. MCI Conversion Prediction Using 3D Zernike Moments and the Improved Dynamic Particle Swarm Optimization Algorithm. Appl. Sci. 2023, 13, 4489. https://doi.org/10.3390/app13074489
Bolourchi P, Gholami M, Moradi M, Beheshti I, Demirel H on behalf of the Alzheimer’s Disease Neuroimaging Initiative. MCI Conversion Prediction Using 3D Zernike Moments and the Improved Dynamic Particle Swarm Optimization Algorithm. Applied Sciences. 2023; 13(7):4489. https://doi.org/10.3390/app13074489
Chicago/Turabian StyleBolourchi, Pouya, Mohammadreza Gholami, Masoud Moradi, Iman Beheshti, and Hasan Demirel on behalf of the Alzheimer’s Disease Neuroimaging Initiative. 2023. "MCI Conversion Prediction Using 3D Zernike Moments and the Improved Dynamic Particle Swarm Optimization Algorithm" Applied Sciences 13, no. 7: 4489. https://doi.org/10.3390/app13074489
APA StyleBolourchi, P., Gholami, M., Moradi, M., Beheshti, I., & Demirel, H., on behalf of the Alzheimer’s Disease Neuroimaging Initiative. (2023). MCI Conversion Prediction Using 3D Zernike Moments and the Improved Dynamic Particle Swarm Optimization Algorithm. Applied Sciences, 13(7), 4489. https://doi.org/10.3390/app13074489