Journals Books 2687-5527
Latest Issue Archive Future Issues About Us
Conference Proceedings

SETSCI - Volume 4(5) (2019)
HORA2019 - International Congress on Human-Computer Interaction, Optimization and Robotic Applications, Ürgüp, Turkey, Jul 05, 2019

An Efficient Medical Diagnosis Algorithm Based on a Hybrid Neural Network with a Variable Adaptive Momentum and PSO Algorithm
Alaa Ali  Hameed1, Naim Ajlouni2, Adem Özyavaş3, Zeynep Orman4*, Ali  Güneş5
1Istanbul University-Cerrahpaşa, Istanbul , Turkey
2Istanbul Aydin University, Istanbul , Turkey
3Istanbul Aydin University, Istanbul , Turkey
4Istanbul University-Cerrahpaşa, Istanbul , Turkey
5Istanbul Aydin University, Istanbul , Turkey
* Corresponding author:
Published Date: 2019-10-12   |   Page (s): 152-157   |    325     20

ABSTRACT Neural Network has been used in a number of scientific fields including medical diagnosis. It is clear that classification is one of the main challenges in any medical diagnosis. However, the drawbacks of conventional neural network classifier are summarized as slow convergence and tendency to be trapped in local minima. The aim of this study is to present a hybrid Back-Propagation algorithm with a variable adaptive momentum (BPVAM) and particle swarm optimization (PSO) (BPVAM-PSO). The PSO is an efficient training algorithm as it does not require any complex calculations. The BPVAM variable momentum, in this case, will start with a high value, which increases the convergence rate; the momentum value will decrease as the error rate is decreased. The best weights of the BPVAM are passed to the PSO. The PSO uses the weights to determine the best set of parameters and as a result, the number of hidden neurons is reduced. This will improve performance further. To verify the efficiency of the BPVAM-PSO algorithm it will be used for the classification of a number of different medical datasets. The results of the proposed algorithm are compared against the performance of the conventional BP Neural Networks and BPVAM algorithm.
KEYWORDS Neural Networks, Particle Swarm, Adaptive momentum, Optimization, Medical Diagnosis
REFERENCES [1] 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.

[2] 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.

[3] 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.

[4] 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.

[5] 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.

[6] 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.

[7] 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.

[8] 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.

[9] 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.

[10] 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.

[11] 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.

[12] 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.

[13] 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.

[14] 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.

[15] D. E. Rumelhart, G. E. Hinton and R. J. Williams, “Learning representations by back-propagating errors”, Cognitive modeling, vol. 5, issue 3, 1, 1988.

[16] 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.

[17] H. Shao and G. Zheng, “Convergence analysis of a back-propagation algorithm with adaptive momentum”, Neurocomputing, vol. 74, issue 5, 749-752, 2011.

[18] 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.

[19] 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.

[20] 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.

[21] 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.

[22] J. Kennedy and R. Eberhart, “Particle Swarm Optimization”, Proceedings of IEEE International Conference on Neural Networks, 1995.

[23] 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.

[24] 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

SET Technology - Turkey

eISSN  : 2687-5527    

E-mail :
+90 533 2245325

Tokat Technology Development Zone Gaziosmanpaşa University Taşlıçiftlik Campus, 60240 TOKAT-TURKEY
©2018 SET Technology