Archimedes Optimization Algorithm with Deep Learning-Based Prostate Cancer Classification on Magnetic Resonance Imaging
Abstract
:1. Introduction
- An intelligent AOADLB-P2C technique is presented, and it comprises AMF-based pre-processing, DenseNet-161-based feature extraction, RMSProp optimizer, LS-SVM classification, and AOA-based hyperparameter tuning. To the best of the researchers’ knowledge, a AOADLB-P2C model has never been presented in the literature.
- A RMSProp optimizer is applied in this study for the selection of hyperparameters involved in the DenseNet-161 model.
- The parameter optimization of the LS-SVM model using the AOA algorithm and cross-validation helps in boosting the predictive outcome of the proposed model for unseen data.
- The performance of the proposed model is validated using a PCa dataset.
2. Related Works
3. The Proposed Model
3.1. Pre-Processing
3.2. Feature Extraction
3.3. Prostate Cancer Classification
4. Results and 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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Class | No. of Instances (Balanced) |
---|---|
Prostate | 200 |
Brachytherapy | 200 |
Total Number of Instances | 400 |
Class | Accuracybal | Sensitivity | Specificity | F-Score | MCC |
---|---|---|---|---|---|
Run-1 | |||||
Prostate | 91.00 | 91.00 | 95.00 | 92.86 | 86.07 |
Brachytherapy | 95.00 | 95.00 | 91.00 | 93.14 | 86.07 |
Average | 93.00 | 93.00 | 93.00 | 93.00 | 86.07 |
Run-2 | |||||
Prostate | 93.50 | 93.50 | 95.50 | 94.44 | 89.02 |
Brachytherapy | 95.50 | 95.50 | 93.50 | 94.55 | 89.02 |
Average | 94.50 | 94.50 | 94.50 | 94.50 | 89.02 |
Run-3 | |||||
Prostate | 94.50 | 94.50 | 95.50 | 94.97 | 90.00 |
Brachytherapy | 95.50 | 95.50 | 94.50 | 95.02 | 90.00 |
Average | 95.00 | 95.00 | 95.00 | 95.00 | 90.00 |
Run-4 | |||||
Prostate | 99.50 | 99.50 | 96.50 | 98.03 | 96.04 |
Brachytherapy | 96.50 | 96.50 | 99.50 | 97.97 | 96.04 |
Average | 98.00 | 98.00 | 98.00 | 98.00 | 96.04 |
Run-5 | |||||
Prostate | 99.00 | 99.00 | 100.00 | 99.50 | 99.00 |
Brachytherapy | 100.00 | 100.00 | 99.00 | 99.50 | 99.00 |
Average | 99.50 | 99.50 | 99.50 | 99.50 | 99.00 |
Methods | Accuracy | Sensitivity | Specificity | F-Score |
---|---|---|---|---|
AOADLB-P2C | 99.50 | 99.50 | 99.50 | 99.50 |
Naïve Bayes | 98.46 | 98.47 | 98.64 | 98.81 |
DT Model | 97.29 | 97.26 | 98.47 | 98.83 |
SVM-Gaussian Model | 98.36 | 98.43 | 98.54 | 97.91 |
SVM-RBF Model | 98.12 | 98.63 | 97.89 | 98.52 |
GoogleNet | 98.28 | 98.28 | 98.49 | 98.69 |
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Share and Cite
Ragab, M.; Kateb, F.; El-Sawy, E.K.; Binyamin, S.S.; Al-Rabia, M.W.; A. Mansouri, R. Archimedes Optimization Algorithm with Deep Learning-Based Prostate Cancer Classification on Magnetic Resonance Imaging. Healthcare 2023, 11, 590. https://doi.org/10.3390/healthcare11040590
Ragab M, Kateb F, El-Sawy EK, Binyamin SS, Al-Rabia MW, A. Mansouri R. Archimedes Optimization Algorithm with Deep Learning-Based Prostate Cancer Classification on Magnetic Resonance Imaging. Healthcare. 2023; 11(4):590. https://doi.org/10.3390/healthcare11040590
Chicago/Turabian StyleRagab, Mahmoud, Faris Kateb, E. K. El-Sawy, Sami Saeed Binyamin, Mohammed W. Al-Rabia, and Rasha A. Mansouri. 2023. "Archimedes Optimization Algorithm with Deep Learning-Based Prostate Cancer Classification on Magnetic Resonance Imaging" Healthcare 11, no. 4: 590. https://doi.org/10.3390/healthcare11040590