Research Article
Peer ReviewedAt least two reviewers
Open Access
An Efficient Medical Diagnosis Algorithm Based on a Hybrid Neural Network with a Variable Adaptive Momentum and PSO Algorithm
1Istanbul University-Cerrahpaşa
2Istanbul Aydin University
✉ Corresponding author: Zeynep Orman · ormanz@istanbul.edu.tr
Proceedings International Congress on Human-Computer Interaction, Optimization and Robotic Applications
Volume 8 Pages 152–157 Year 2019
References
- J. Xu, P. Wu, Y. Chen, Q. Meng, H. Dawood and M. M. Khan, “A Novel Deep Flexible Neural Forest Model for Classification of Cancer Subtypes Based on Gene Expression Data”, IEEE Access, vol. 7, pp. 22086-22095, 2019.
- D. P. Aldryan and A. Annisa, “Cancer Detection Based on Microarray Data Classification with Ant Colony Optimization and Modified Backpropagation Conjugate Gradient Polak-Ribiére”, In 2018 International Conference on Computer, Control, Informatics and its Applications (IC3INA), pp. 13-16, 2018.
- S. Reis, P. Gazinska, J. H. Hipwell, T. Mertzanidou, K. Naidoo, N. Williams, S. Pinder and D. J. Hawkes, “Automated classification of breast cancer stroma maturity from histological images”, IEEE Transactions on Biomedical Engineering, vol. 64, issue 10, pp. 2344-2352, 2017.
- F. F. Ting and K. S. Sim, “Self-regulated multilayer perceptron neural network for breast cancer classification”, In 2017 International Conference on Robotics, Automation and Sciences (ICORAS), pp. 1-5,2017.
- N. Yukinawa, S. Oba, K. Kato and S. Ishii, “Optimal aggregation of binary classifiers for multiclass cancer diagnosis using gene expression profiles”, IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 6, issue 2, pp. 333-343, 2008.
- R. Jafari-Marandi, S. Davarzani, M. S. Gharibdousti and B. K. Smith, “An optimum ANN-based breast cancer diagnosis: Bridging gaps between ANN learning and decision-making goals”, Applied Soft Computing, vol. 72, pp. 108-120, 2018.
- F. Zhanga, Z. Li, B. Zhang, H. Du, B. Wang and X. Zhang, Multimodal Deep Learning Model for Auxiliary Diagnosis of Alzheimer’s Disease. Neurocomputing, 2019.
- S. E. Lacy, S. L. Smith and M. A. Lones, “Using echo state networks for classification: A case study in Parkinson's disease diagnosis”, Artificial intelligence in medicine, vol. 86, pp. 53-59, 2018.
- A. L. Samuel, “Some studies in machine learning using the game of checkers. II—recent progress”, In Computer Games I, pp. 366-400. Springer, New York, NY, 1988.
- Y. Fei, G. Pengdong and L. Yongquan, “Bolt force prediction using simplified finite element model and back propagation neural networks”, In 2016 IEEE Information Technology, Networking, Electronic and Automation Control Conference, pp. 520-523, 2016.
- Y. Zhang and Z. Wang, “A hybrid model for blood pressure prediction from a PPG signal based on MIV and GA-BP neural network”, In 2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNCFSKD), pp. 1989-1993, 2017.
- Y. Liu, W. Jing and L. Xu, “Cascading model based back propagation neural network in enabling precise classification”, In 2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), pp. 7-11, 2016.
- Y. Fang, M. Pang and B. Wang, “A course control system of unmanned surface vehicle (USV) using back-propagation neural network (BPNN) and artificial bee colony (ABC) algorithm”, Procedia computer science, vol. 111, pp. 361-366, 2017.
- N. Bisoyi, H. Gupta, N. P. Padhy and G. J. Chakrapani, “Prediction of daily sediment discharge using a back propagation neural network training algorithm: A case study of the Narmada River, India”, International journal of sediment research, vol. 34, issue 2, pp. 125-135, 2019.
- D. E. Rumelhart, G. E. Hinton and R. J. Williams, “Learning representations by back-propagating errors”, Cognitive modeling, vol. 5, issue 3, 1, 1988.
- H. Shao and G. Zheng, “A new BP algorithm with adaptive momentum for FNNs training”, In 2009 WRI Global Congress on Intelligent Systems, vol. 4, pp. 16-20, 2009.
- H. Shao and G. Zheng, “Convergence analysis of a back-propagation algorithm with adaptive momentum”, Neurocomputing, vol. 74, issue 5, 749-752, 2011.
- A. A. Hameed, B. Karlik and M. S. Salman, “Back-propagation algorithm with variable adaptive momentum”, Knowledge-Based Systems, vol. 114, pp. 79-87, 2016.
- Y. Ding, W. Zhang, L. Yu and K. Lu, “The accuracy and efficiency of GA and PSO optimization schemes on estimating reaction kinetic parameters of biomass pyrolysis”, Energy, vol. 176, pp. 582-588, 2019.
- H. Hamdi, C. B. Regaya and A. Zaafouri, “Real-time study of a photovoltaic system with boost converter using the PSO-RBF neural network algorithms in a MyRio controller”, Solar Energy, vol. 183, pp. 1-16, 2019.
- H. Liang, J. Zou, Z. Li, M. J. Khan and Y. Lu, “Dynamic evaluation of drilling leakage risk based on fuzzy theory and PSO-SVR algorithm”, Future Generation Computer Systems, vol. 95, pp. 454- 466, 2019.
- J. Kennedy and R. Eberhart, “Particle Swarm Optimization”, Proceedings of IEEE International Conference on Neural Networks, 1995.
- H. Cui, M. Shu, M. Song and Y. Wang, “Parameter selection and performance comparison of particle swarm optimization in sensor networks localization”, Sensors, vol. 17, issue 3, 487, 2017.
- D.E. Rumelhart, J. L. McClelland and the PDP Research Group. Parallel Distributed Processing Explorations in the Microstructure of Cognition. Mass.: MIT Press, 1986. 15
Cited by
2 — Crossref — cited-by count (retrieved 2 Oct 2026, source)
Citation counts are shown with their source and retrieval date. They are not an Impact Factor or any journal-level metric.