Skip to main content
SpringerLink
Log in

Fundamentals of artificial metaplasticity in radial basis function networks for breast cancer classification

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Modern medicine generates data commonly used for the development of clinical decision support systems, whose usefulness often lies in the performance of the machine learning algorithms used for the processing of that data. Several lines of research seek to resemble artificial neural networks to biological ones by incorporating new bioinspired mechanisms. One of these mechanisms is the biological concept of metaplasticity, defined as the plasticity of synaptic plasticity and which has been shown to be directly related to learning and memory. It has also been shown that incorporating this mechanism into a multilayer perceptron improves the neural network performance in both accuracy and learning rate when diagnosing breast cancer. The early detection of breast cancer is one of the most important strategies to prevent deaths from this disease. In this work, we have modeled synaptic metaplasticity in a radial base function network, which converges faster than multilayer perceptrons, with the motivation to achieve a more accurate solution in the diagnosis of breast cancer.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Abdel-Basset M, El-Shahat D, El-henawy I, de Albuquerque VHC, Mirjalili S (2020) A new fusion of grey wolf optimizer algorithm with a two-phase mutation for feature selection. Expert Syst Appl 139:112824. https://doi.org/10.1016/j.eswa.2019.112824

    Article  Google Scholar 

  2. Abdel-Zaher AM, Eldeib AM (2016) Breast cancer classification using deep belief networks. Expert Syst Appl 46:139–144. https://doi.org/10.1016/j.eswa.2015.10.015, https://www.sciencedirect.com/science/article/pii/S0957417415007101

  3. Abraham WC (1996) Activity-dependent regulation of synaptic plasticity (metaplasticity) in the hippocampus. Funct Clin Relev Hippocampus 5–26

  4. Abraham WC (2008) Metaplasticity: tuning synapses and networks for plasticity. https://doi.org/10.1038/nrn2356, www.nature.com/reviews/neuro

  5. Abraham WC, Bear MF (1996) Metaplasticity: The plasticity of synaptic plasticity. Trends Neurosci 19(4):126–130. https://doi.org/10.1016/S0166-2236(96)80018-X

    Article  Google Scholar 

  6. Al-Quraishi T, Abawajy JH, Chowdhury MU, Rajasegarar S, Abdalrada AS (2018) Breast cancer recurrence prediction using random forest model. Adv intell Syst Comput 700:318–329. https://doi.org/10.1007/978-3-319-72550-5_31

    Article  Google Scholar 

  7. Andina D, Pham DT (2008) Computational intelligence: for engineering and manufacturing. Springer, US. https://doi.org/10.1007/0-387-37452-3

  8. Andina D, Álvarez-Vellisco A, Aleksandar J, Fombellida J (2009) Artificial metaplasticity can improve artificial neural networks learning. Intell Autom Soft Comput 15(4):683–696. https://doi.org/10.1080/10798587.2009.10643057

    Article  Google Scholar 

  9. Arican M, Polat K (2020) Binary particle swarm optimization (BPSO) based channel selection in the EEG signals and its application to speller systems. J Artif Intell Syst 2(1):27–37, https://doi.org/10.33969/ais.2020.21003

  10. Bear MF, Malenka RC (1994) Synaptic plasticity: LTP and LTD. Curr Opin Neurobiol 4(3):389–399. https://doi.org/10.1016/0959-4388(94)90101-5

    Article  Google Scholar 

  11. Bhardwaj A, Tiwari A (2015) Breast cancer diagnosis using genetically optimized neural network model. Expert Syst Appl 42(10):4611–4620. https://doi.org/10.1016/j.eswa.2015.01.065

    Article  Google Scholar 

  12. Cai L, Gao J, Zhao D (2020) A review of the application of deep learning in medical image classification and segmentation. Anna Trans Med 8(11):713–713

  13. Daldal N, Sengur A, Polat K, Cömert Z (2020) A novel demodulation system for base band digital modulation signals based on the deep long short-term memory model. Appl Acoust 166:107346. https://doi.org/10.1016/j.apacoust.2020.107346

    Article  Google Scholar 

  14. Dalwinder S, Birmohan S, Manpreet K (2020) Simultaneous feature weighting and parameter determination of Neural Networks using Ant Lion Optimization for the classification of breast cancer. Biocybern Biomed Eng 40(1):337–351. https://doi.org/10.1016/j.bbe.2019.12.004

    Article  Google Scholar 

  15. Dora L, Agrawal S, Panda R, Abraham A (2017) Optimal breast cancer classification using Gauss-Newton representation based algorithm. Expert Syst Appl 85:134–145. https://doi.org/10.1016/j.eswa.2017.05.035

    Article  Google Scholar 

  16. Fombellida J, Martin-Rubio I, Andina D (2016) Application of artificial metaplasticity fundamentals to WBCD breast cancer database classification method. In: World automation congress proceedings, IEEE Computer Society. https://doi.org/10.1109/WAC.2016.7582981

  17. Fu Y, Lei Y, Wang T, Curran WJ, Liu T, Yang X (2020). Deep learning in medical image registration: a review. https://doi.org/10.1088/1361-6560/ab843e

  18. Ginsburg O, Yip C, Brooks A, Cabanes A, Caleffi M, Dunstan Yataco JA, Gyawali B, McCormack V, de McLaughlin Anderson M, Mehrotra R, Mohar A, Murillo R, Pace LE, Paskett ED, Romanoff A, Rositch AF, Scheel JR, Schneidman M, Unger-Saldaña K, Vanderpuye V, Wu T, Yuma S, Dvaladze A, Duggan C, Anderson BO (2020) Breast cancer early detection: a phased approach to implementation. Cancer 126(S10):2379–2393. https://doi.org/10.1002/cncr.32887

    Article  Google Scholar 

  19. Haykin S (2001) Neural networks and learning machines, 3rd edn, vol 40. http://doi.wiley.com/10.1002/1521-3773(20010316)40:6%3C9823::AID-ANIE9823%3E3.3.CO;2-C

  20. Kinouchi O, Pazzini R, Copelli M (2020) Mechanisms of self-organized quasicriticality in neuronal network models. Front Phys 8:530. https://doi.org/10.3389/fphy.2020.583213

  21. Kiseľák J, Lu Y, Švihra J, Szépe P, Stehlík M (2020) “SPOCU”: scaled polynomial constant unit activation function. Neural Comput Appl. https://doi.org/10.1007/s00521-020-05182-1

  22. Marcano-Cedeño A, Álvarez-Vellisco A, Andina D (2009) Artificial metaplasticity MLP applied to image classification. In: IEEE international conference on industrial informatics (INDIN), pp 650–653, https://doi.org/10.1109/INDIN.2009.5195879

  23. Marcano-Cedeño A, Quintanilla-Domínguez J, Andina D (2011a) Breast cancer classification applying artificial metaplasticity algorithm. Neurocomputing 74(8):1243–1250. https://doi.org/10.1016/j.neucom.2010.07.019

    Article  Google Scholar 

  24. Marcano-Cedeño A, Quintanilla-Domínguez J, Andina D (2011b) WBCD breast cancer database classification applying artificial metaplasticity neural network. Expert Syst Appl 38(8):9573–9579. https://doi.org/10.1016/j.eswa.2011.01.167

    Article  Google Scholar 

  25. van Melle W (1978) MYCIN: a knowledge-based consultation program for infectious disease diagnosis. Int J Man-Mach Stud 10(3):313–322. https://doi.org/10.1016/S0020-7373(78)80049-2

    Article  Google Scholar 

  26. Moody J, Darken CJ (1989) Fast Learning in Networks of Locally-Tuned Processing Units. Neural Comput 1(2):281–294. https://doi.org/10.1162/neco.1989.1.2.281

    Article  Google Scholar 

  27. Ontiveros-Robles E, Melin P (2019) A hybrid design of shadowed type-2 fuzzy inference systems applied in diagnosis problems. Eng Appl Artif Intell 86:43–55. https://doi.org/10.1016/j.engappai.2019.08.017

    Article  Google Scholar 

  28. Ozdemir A, Polat K (2020) Deep learning applications for hyperspectral imaging: a systematic review. J Inst Electron Comput 2(1):39–56, https://doi.org/10.33969/jiec.2020.21004

  29. Patrício M, Pereira J, Crisóstomo J, Matafome P, Gomes M, Seiça R, Caramelo F (2018) Using resistin, glucose, age and BMI to predict the presence of breast cancer. BMC Cancer 18(1):29. https://doi.org/10.1186/s12885-017-3877-1

    Article  Google Scholar 

  30. Pelàez JR, Castillo Piqueira JR (2008) Biological clues for up-to-date artificial neurons. In: Computational intelligence: for engineering and manufacturing. Springer, US, pp 131–146. https://doi.org/10.1007/0-387-37452-3_6

  31. Polat K, Onur Koc K (2020) Detection of skin diseases from dermoscopy image using the combination of convolutional neural network and one-versus-all. J Artif Intell Syst 2(1):80–97, https://doi.org/10.33969/ais.2020.21006

  32. Ruck DW, Rogers SK, Kabrisky M, Oxley ME, Suter BW (1990) The multilayer perceptron as an approximation to a bayes optimal discriminant function. IEEE Trans Neural Netw 1(4):296–298. https://doi.org/10.1109/72.80266

    Article  Google Scholar 

  33. Schwenker F, Kestler HA, Palm G (2001) Three learning phases for radial-basis-function networks. Neural Netw 14(4–5):439–458. https://doi.org/10.1016/S0893-6080(01)00027-2

    Article  MATH  Google Scholar 

  34. Sheikhpour R, Sarram MA, Sheikhpour R (2016) Particle swarm optimization for bandwidth determination and feature selection of kernel density estimation based classifiers in diagnosis of breast cancer. Appl Soft Comput J 40:113–131. https://doi.org/10.1016/j.asoc.2015.10.005

    Article  MATH  Google Scholar 

  35. Tajbakhsh N, Jeyaseelan L, Li Q, Chiang JN, Wu Z, Ding X (2020) Embracing imperfect datasets: a review of deep learning solutions for medical image segmentation. Med Image Anal 63:101693. https://doi.org/10.1016/j.media.2020.101693

    Article  Google Scholar 

  36. Wang H, Zheng B, Yoon SW, Ko HS (2018) A support vector machine-based ensemble algorithm for breast cancer diagnosis. Eur J Oper Res 267(2):687–699. https://doi.org/10.1016/j.ejor.2017.12.001

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Technology, University of Alicante, Carretera San Vicente s/n, 03690, San Vicente del Raspeig, Alicante, Spain

    Víctor Vives-Boix & Daniel Ruiz-Fernández

Authors
  1. Víctor Vives-Boix
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Daniel Ruiz-Fernández
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Daniel Ruiz-Fernández.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Vives-Boix, V., Ruiz-Fernández, D. Fundamentals of artificial metaplasticity in radial basis function networks for breast cancer classification. Neural Comput & Applic 33, 12869–12880 (2021). https://doi.org/10.1007/s00521-021-05938-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-021-05938-3

Keywords

Search

Navigation