Hybrid Whale and Gray Wolf Deep Learning Optimization Algorithm for Prediction of Alzheimer’s Disease
Abstract
:1. Introduction
2. Literature Review
3. Proposed Framework Methodologies
3.1. Dataset Availability
3.2. Preprocessing
3.3. Segmentation
3.3.1. Whale Optimization Algorithm (WOA)
3.3.2. Gray Wolf Optimization (GWO)
3.3.3. Hybrid WOA and GWO
Algorithm 1. Pseudocode |
S1: Initial population of the SA has been generated S2: Determine the fitness function for each search agent. S3: : first best solution S4: : second best solution S5: : third best solution S6: if1 n < max no. of iterations S7: for i = 1 to no. of search agent S8: update the parameters of A, C, a, l, and p S9: if2 (p1 < 0.5) S10: if3 (|A| < 1) S11: search agent position has been updated. S12: else if3 (|A| ≥ 1) S13: random agent selection () and update the search agent position. S:14: end if3 S15: else if2 (p1 ≥ 0.5) and update the position of the search agent. S16: end if2 S17: checks the search agent position whether it has gone outside of the assigned search space. S18: positions of , , are updated. S19: update the number of iterations S20: end if S21: R |
3.3.4. Ground-Truth Validation
3.4. Classification
3.5. Clinical Score Validation
4. Results and Discussion
Implementation of Hybrid Technique
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Particulars | NC | AD |
---|---|---|
Number of datasets | 100 | 100 |
Sex (M/F) | 73/27 | 68/32 |
Age group | 60.5 ± 5.5 | 67.5 ± 7.5 |
Clinical Dementia Rate (CDR) | 0 | 1 and 2 |
Mini-Mental Score Examination value | 26 ± 3.5 | 16 ± 2.5 |
Parameters | WOA | HWGO | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
Ven | GM | WM | CC | HC | Ven | GM | WM | CC | HC | |
No. of SAs | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 |
I | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 |
T1 | 8 | 5 | 5 | 10 | 13 | 8 | 5 | 5 | 10 | 13 |
Statistical Measures | WOA | HWGO | WOA | HWGO | WOA | HWGO | WOA | HWGO | WOA | HWGO |
---|---|---|---|---|---|---|---|---|---|---|
GM | WM | Ventricle | CC | HC | ||||||
Accuracy | 0.88 | 0.91 | 0.89 | 0.91 | 0.89 | 0.91 | 0.912 | 0.934 | 0.92 | 0.94 |
Sensitivity | 0.89 | 0.90 | 0.89 | 0.90 | 0.89 | 0.90 | 0.90 | 0.915 | 0.90 | 0.93 |
Specificity | 0.88 | 0.90 | 0.88 | 0.90 | 0.88 | 0.90 | 0.91 | 0.91 | 0.91 | 0.91 |
Optimization Algorithm | WOA | GWO | HWGO |
---|---|---|---|
Number of iterations to attain optimal solutions/maximum number of iterations | 82/100 | 62/100 | 75/100 |
Time complexity | 30.425 | 23.782 | 26.782 |
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Dhakhinamoorthy, C.; Mani, S.K.; Mathivanan, S.K.; Mohan, S.; Jayagopal, P.; Mallik, S.; Qin, H. Hybrid Whale and Gray Wolf Deep Learning Optimization Algorithm for Prediction of Alzheimer’s Disease. Mathematics 2023, 11, 1136. https://doi.org/10.3390/math11051136
Dhakhinamoorthy C, Mani SK, Mathivanan SK, Mohan S, Jayagopal P, Mallik S, Qin H. Hybrid Whale and Gray Wolf Deep Learning Optimization Algorithm for Prediction of Alzheimer’s Disease. Mathematics. 2023; 11(5):1136. https://doi.org/10.3390/math11051136
Chicago/Turabian StyleDhakhinamoorthy, Chitradevi, Sathish Kumar Mani, Sandeep Kumar Mathivanan, Senthilkumar Mohan, Prabhu Jayagopal, Saurav Mallik, and Hong Qin. 2023. "Hybrid Whale and Gray Wolf Deep Learning Optimization Algorithm for Prediction of Alzheimer’s Disease" Mathematics 11, no. 5: 1136. https://doi.org/10.3390/math11051136
APA StyleDhakhinamoorthy, C., Mani, S. K., Mathivanan, S. K., Mohan, S., Jayagopal, P., Mallik, S., & Qin, H. (2023). Hybrid Whale and Gray Wolf Deep Learning Optimization Algorithm for Prediction of Alzheimer’s Disease. Mathematics, 11(5), 1136. https://doi.org/10.3390/math11051136