A hybrid artificial bee colony with whale optimization algorithm for improved breast cancer diagnosis | Neural Computing and Applications Skip to main content
Springer Nature Link
Log in

A hybrid artificial bee colony with whale optimization algorithm for improved breast cancer diagnosis

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

Abstract

Breast cancer is the most common among women that leads to death if not diagnosed at early stages. Early diagnosis plays a vital role in decreasing the mortality rate globally. Manual methods for diagnosing breast cancers suffer from human errors and inaccuracy, and consume time. A computer-aided diagnosis (CAD) can overcome the disadvantages of manual methods and helps radiologists for accurate decision-making. A CAD system based on artificial neural network (ANN) optimized using a swarm-based approach can improve the accuracy of breast cancer diagnosis due to its strong prediction capabilities. Artificial bee colony (ABC) and whale optimization are metaheuristic search algorithms used to solve combinatorial optimization problems. This paper proposes a hybrid artificial bee colony with whale optimization algorithm (HAW) by integrating the exploitative employee bee phase of ABC with the bubble net attacking method of whale optimization to propose an employee bee attacking phase. In the employee bee attacking phase, employee bees use exploitation of humpback whales for finding better food source positions. The weak exploration of standard ABC is improved using the proposed mutative initialization phase that forms the explorative phase of the HAW algorithm. HAW algorithm is used in simultaneous feature selection (FS) and parameter optimization of an ANN model. HAW is implemented using backpropagation learning that includes resilient backpropagation (HAW-RP), Levenberg–Marquart (HAW-LM) and momentum-based gradient descent (HAW-GD). These hybrid variants are evaluated using various breast cancer datasets in terms of accuracy, complexity and computational time. HAW-RP variant achieved higher accuracy of 99.2%, 98.5%, 96.3%, 98.8%, 98.7% and 99.1% with low-complexity ANN model when compared to HAW-LM and HAW-GD for WBCD, WDBC, WPBC, DDSM, MIAS and INbreast, respectively.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. American Cancer Society (2018) Global Cancer: Facts & Figures, 4th edition http://www.cancer.org/content/dam/cancer-org/research/cancer-facts-and-statistics/global-cancer-facts-and-figures/global-cancer-facts-and-figures-4th-edition.pdf.

  2. Eltoukhy MM, Faye I, Samir BB (2010) A comparison of wavelet and curvelet for breast cancer diagnosis in digital mammogram. Comput Biol Med 40(4):384–391

    Article  Google Scholar 

  3. Zhao M, Fu C, Ji L, Tang K, Zhou M (2011) FS and parameter optimization for support vector machines: A new approach based on genetic algorithm with feature chromosomes. Expert Syst Appl 38(5):5197–5204

    Article  Google Scholar 

  4. Fu JC, Lee SK, Wong STC, Yeh JY, Wang AH, Wu HK (2005) Image segmentation, feature selection and pattern classification for mammographic microcalcifications. Comput Med Imaging Graph 29:419–429

    Article  Google Scholar 

  5. Ghoncheh M, Pournamdar Z, Salehiniya H (2016) Incidence and mortality and epidemiology of breast cancer in the world. Asian Pac J Cancer Prev 17(S3):43–46

    Article  Google Scholar 

  6. Übeyli ED (2007) Implementing automated diagnostic systems for breast cancer detection. Expert Syst Appl 33(4):1054–1062

    Article  Google Scholar 

  7. Karabatak M, Ince MC (2009) An expert system for detection of breast cancer based on association rules and neural network. Expert Syst Appl 36(2):3465–3469

    Article  Google Scholar 

  8. Punitha S, Amuthan A, Suresh Joseph K (2019) enhanced monarchy butterfly optimization Technique for effective breast cancer diagnosis. J Med Syst 43(7):1–14

    Article  Google Scholar 

  9. Thompson K, Suresh Joseph K (2018) Particle swarm optimization-based energy efficient channel assignment technique for clustered cognitive radio sensor networks. Comput J Oxford Univ Press 61(6):926–936

    Google Scholar 

  10. Thompson S, Suresh Joseph K (2016) PSO assisted OLSR routing for cognitive radio vehicular sensor networks. In: ACM international conference on informatics and analytics (pp. 1–8)

  11. Colorni A, Dorigo M, Maniezzo V (1991) Distributed optimization by ant colonies. In: Proceedings of the first European conference on artificial life (pp. 134–42)

  12. Eberhart RC, Kennedy J (1995) A new optimizer using particle swarm theory. In: Proceedings of the sixth international symposium on micro machine and human science (pp. 39–43)

  13. Yang XS (2010) A new metaheuristic bat-inspired algorithm. In: Nature inspired cooperative strategies for optimization (NICSO 2010) (pp. 65–74). Springer; New York

  14. Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numericalfunction optimization: artificial bee colony (ABC) algorithm. J Global Optim 39:459–471

    Article  MathSciNet  MATH  Google Scholar 

  15. Mirjalili S, Mirjalili S, Hatamlou A (2015) Multi-verse optimizer: a nature-inspiredalgorithm for global optimization. Neural Comput Appl 1–19 /03/172015

  16. Gandomi AH, Alavi AH (2012) Krill herd: A new bio-inspired optimization algorithm. Commun Nonlinear Sci Numer Simul 17(12):4831–4845

    Article  MathSciNet  MATH  Google Scholar 

  17. Wang G-G, Deb S, Cui Z (2015) Monarch butterfly optimization. Neural Comput Appl 28(3):1–20

    Google Scholar 

  18. P. K. M (2002) Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Syst 22(3), 52–67

  19. Castro L, Timmis JI (2003) Artificial immune systems as a novel soft computing paradigm. J Soft Comput 7: 526

  20. Gandomi AH (2014) Interior search algorithm (ISA): A novel approach for global optimization. ISA Trans 53(4):1168–1183

    Article  Google Scholar 

  21. Mirjalili S, Gandomi AH, Mirjalili SZ, Saremi S, Faris H, Mirjalili SM (2017) Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems. Adv Eng Softw 114:163–191

    Article  Google Scholar 

  22. Faramarzi A, Heidarinejad M, Mirjalili S, Gandomi AH (2020) Marine Predators Algorithm: A Nature-inspired Metaheuristic. Expert Syst Appl 113377

  23. Furundzic D, Djordjevic M, Bekic AJ (1998) Neural networks approach to early breast cancer detection. J Syst Architect 44(8):617–633

    Article  Google Scholar 

  24. Kolen JF, Pollack JB (1991) Back propagation is sensitive to initial conditions. Adv Neural Inf Process Syst 3:860–867

    Google Scholar 

  25. Ferentinos KP (2005) Biological engineering applications of feedforward neural networks designed and parameterized by genetic algorithms. Neural Netw 18(7):934–950

    Article  MathSciNet  Google Scholar 

  26. Setiono R, Liu H (1997) Neural-network feature selector. IEEE Trans Neural Networks 8(3):654–662

    Article  Google Scholar 

  27. Verikas A, Bacauskiene M (2002) FS with neural networks. Pattern Recogn Lett 23(11):1323–1335

    Article  MATH  Google Scholar 

  28. Kabir MM, Islam MM, Murase K (2010) A new wrapper FS approach using neural network. Neurocomputing 73(16–18):3273–3283

    Article  Google Scholar 

  29. Telikani A, Gandomi AH, Shahbahrami A, Dehkordi MN (2020) Privacy-preserving in association rule mining using an improved discrete binary artificial bee colony. Expert Syst Appl 144:113097

    Article  Google Scholar 

  30. Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67

    Article  Google Scholar 

  31. Shunmugapriya P, Kanmani S (2017) A hybrid algorithm using ant and bee colony optimization for FS and classification (AC-ABC Hybrid). Swarm Evol Comput 36:27–36

    Article  Google Scholar 

  32. Zorarpacı E, Özel SA (2016) A hybrid approach of differential evolution and artificial bee colony for FS. Expert Syst Appl 62:91–103

    Article  Google Scholar 

  33. Shanthi S, Bhaskaran VM (2014) “Modified artificial bee colony-based FS: A new method in the application of mammogram image classification.” Int J Sci Eng Tech Res (IJSETR) 3(6):1664–1667

    Google Scholar 

  34. Rao H, Shi X, Rodrigue AK, Feng J, Xia Y, Elhoseny M, Yuan X, Gu L (2019) FS based on artificial bee colony and gradient boosting decision tree. Appl Soft Comput 74:634–642

    Article  Google Scholar 

  35. Badem H, Basturk A, Caliskan A, Yuksel ME (2017) A new efficient training strategy for deep neural networks by hybridization of artificial bee colony and limited–memory BFGS optimization algorithms. Neurocomputing 266:506–526

    Article  Google Scholar 

  36. Garro BA, Rodríguez K, Vázquez RA (2016) Classification of DNA microarrays using ANNs and ABC algorithm. Appl Soft Comput 38:548–560

    Article  Google Scholar 

  37. Palanisamy S, Kanmani S (2012) Artificial Bee Colony Approach for Optimizing FS. Int J Comput Sci Issue 9(3):432–438

    Google Scholar 

  38. Schiezaro M, Pedrini H (2013) Data FS based on Artificial Bee Colony algorithm. EURASIP J Image Video Process 1:2013

    Google Scholar 

  39. Djellali H, Djebbar A, Zine NG, Azizi N (2018) Hybrid Artificial Bees Colony and Particle Swarm on FS. In: Computational intelligence and its applications IFIP advances in information and communication technology (pp. 93–105)

  40. Nagarajan G, Minu R, Muthukumar B, Vedanarayanan V, Sundarsingh S (2016) Hybrid genetic algorithm for medical image feature extraction and selection. Proc Comput Sci 85:455–462

    Article  Google Scholar 

  41. Sayed GI, Darwish A, Hassanien AE, Pan JS (2016) Breast cancer diagnosis approach based on meta-heuristic optimization algorithm inspired by the bubble-net hunting strategy of Whales. In: Advances in intelligent systems and computing genetic and evolutionary computing (pp. 306–313)

  42. Jona J, Nagaveni N (2014) Ant-cuckoo Colony Optimization for FS in Digital Mammogram. Pak J Biol Sci 17(2):266–271

    Article  Google Scholar 

  43. Prechelt L (1994) Proben1: a set of neural network benchmark problems and benchmarking rules. Technical Report, University of Karlsruhe, Karlsruhe, Germany

  44. Quinlan JR (1996) Improved Use of Continuous Attributes in C4.5. J Artific Intell Res 4:77–90

    Article  MATH  Google Scholar 

  45. Hamilton HJ, Shan N, Cercone N (1996) RIAC: a rule induction algorithm based on approximate classification. In: International conference on engineering applications of neural networks, University of Regina

  46. Nauck D, Kruse R (1999) Obtaining interpretable fuzzy classification rules from medical data. Artif Intell Med 16(2):149–169

    Article  Google Scholar 

  47. Peña-Reyes CA, Sipper M (1999) A fuzzy-genetic approach to breast cancer diagnosis. Artif Intell Med 17(2):131–155

    Article  Google Scholar 

  48. Setiono R (2000) Generating concise and accurate classification rules for breast cancer diagnosis. Artif Intell Med 18(3):205–219

    Article  Google Scholar 

  49. Albrecht A, Lappas G, Vinterbo S, Wong C, Ohno-Machado L (2002) Two applications of the LSA machine. In: Proceedings of the 9th international conference on neural information processing (pp. 184–189). ICONIP 02

  50. Fogel DB, Wasson EC, Boughton EM (1995) Evolving neural networks for detecting breast cancer. Cancer Lett 96(1):49–53

    Article  Google Scholar 

  51. Abonyi J, Szeifert F (2003) Supervised fuzzy clustering for the identification of fuzzy classifiers. Pattern Recogn Lett 24(14):2195–2207

    Article  MATH  Google Scholar 

  52. Polat K, Güneş S (2007) Breast cancer diagnosis using least square support vector machine. Dig Sign Process 17(4):694–701

    Article  Google Scholar 

  53. Guijarro-Berdiñas B, Fontenla-Romero O, Pérez-Sánchez B, Fraguela P (2007) “A linear learning method for multilayer perceptrons using least-squares”, intelligent data engineering and automated learning - ideaL. Lect Notes Comput Sci 4881:365–374

    Article  Google Scholar 

  54. Stoean R, Stoean C (2013) Modeling medical decision making by support vector machines, explaining by rules of evolutionary algorithms with FS. Expert Syst Appl 40(7):2677–2686

    Article  MathSciNet  Google Scholar 

  55. Ahmad F, Isa NAM, Hussain Z, Osman MK, Sulaiman SN (2014) A GA-based FS and parameter optimization of an ANN in diagnosing breast cancer. Pattern Anal Appl 18(4):861–870

    Article  Google Scholar 

  56. Karthik S, Perumal RS, Chandra Mouli PVSSR (2018) Breast cancer classification using deep neural networks. In: Knowledge Computing and Its Applications (pp. 227–241)

  57. Bamakan SMH, Gholami P (2014) A novel FS method based on an integrated data envelopment analysis and entropy model. Proc Comput Sci 31:632–638

    Article  Google Scholar 

  58. Xue B, Zhang M, Browne WN (2012) New fitness functions in binary particle swarm optimisation for FS. In: 2012 IEEE congress on evolutionary computation (pp. 1–8)

  59. Xue B, Zhang M, Browne WN (2014) Particle swarm optimisation for FS in classification: Novel initialisation and updating mechanisms. Appl Soft Comput 18:261–276

    Article  Google Scholar 

  60. Maldonado S, Weber R, Basak J (2011) Simultaneous FS and classification using kernel-penalized support vector machines. Inf Sci 181(1):115–128

    Article  Google Scholar 

  61. Miao D, Gao C, Zhang N, Zhang Z (2011) Diverse reduct subspaces-based co-training for partially labeled data. Int J Approx Reason 52(8):1103–1117

    Article  MathSciNet  Google Scholar 

  62. Luukka P, Leppälampi T (2006) Similarity classifier with generalized mean applied to medical data. Comput Biol Med 36(9):1026–1040

    Article  Google Scholar 

  63. Sheikhpour R, Sarram MA, Sheikhpour R (2016) Particle swarm optimization for bandwidth determination and FS of kernel density estimation-based classifiers in diagnosis of breast cancer. Appl Soft Comput 40:113–131

    Article  MATH  Google Scholar 

  64. Belciug S, Gorunescu F (2012) A hybrid neural network/genetic algorithm applied to breast cancer detection and recurrence. Expert Syst 30(3):243–254

    Article  Google Scholar 

  65. Salama GI, Abdelhalim MB, Zeid MAE (2012) Experimental comparison of classifiers for breast cancer diagnosis. In: 2012 Seventh International Conference on Computer Engineering & Systems (ICCES)

  66. Sridevi T, Murugan A (2014) A novel FS method for effective breast cancer diagnosis and prognosis. Int J Comput Appl 88(11):28–33

    Google Scholar 

  67. Wang W, Yang L-J, Xie Y-T, An Y-W (2014) Edge detection of infrared image with CNN_DGA algorithm. Optik 125(1):464–467

    Article  Google Scholar 

  68. Liu X, Tang J (2014) Mass Classification in Mammograms Using Selected Geometry and Texture Features, and a New SVM-Based FS Method. IEEE Syst J 8(3):910–920

    Article  Google Scholar 

  69. Saki F, Tahmasbi A, Soltanian-Zadeh H, Shokouhi SB (2013) Fast opposite weight learning rules with application in breast cancer diagnosis. Comput Biol Med 43(1):32–41

    Article  Google Scholar 

  70. Buciu I, Gacsadi A (2011) Directional features for automatic tumor classification of mammogram images. Biomed Signal Process Control 6(4):370–378

    Article  Google Scholar 

  71. Tahmasbi A, Saki F, Shokouhi SB (2011) Classification of benign and malignant masses based on Zernike moments. Comput Biol Med 41(8):726–735

    Article  Google Scholar 

  72. Tahmasbi A, Saki F, Shokouhi SB (2010) Mass diagnosis in mammographyimages using novel FTRD features. In: 2010 17th Iranian Conference of Biomedical Engineering (ICBME)

  73. Zhang Y, Tomuro N, Furst J, Raicu DS (2011) Building an ensemble system for diagnosing masses in mammograms. Int J Comput Assist Radiol Surg 7(2):323–329

    Article  Google Scholar 

  74. Verma B, Mcleod P, Klevansky A (2010) Classification of benign and malignant patterns in digital mammograms for the diagnosis of breast cancer. Expert Syst Appl 37(4):3344–3351

    Article  Google Scholar 

  75. Verma B, Mcleod P, Klevansky A (2009) A novel soft cluster neural network for the classification of suspicious areas in digital mammograms. Pattern Recogn 42(9):1845–1852

    Article  MATH  Google Scholar 

  76. Rojas-Dominguez A, Nandi AK (2009) Development of tolerant features for characterization of masses in mammograms. Comput Biol Med 39(8):678–688

    Article  Google Scholar 

  77. Dheeba J, Selvi ST (2011) A swarm optimized neural network system for classification of microcalcification in mammograms. J Med Syst 36(5):3051–3061

    Article  Google Scholar 

  78. Dheeba J, Singh NA, Selvi ST (2014) Computer-aided detection of breast cancer on mammograms: A swarm intelligence optimized wavelet neural network approach. J Biomed Inform 49:45–52

    Article  Google Scholar 

  79. Rouhi R, Jafari M, Kasaei S, Keshavarzian P (2015) Benign and malignant breast tumors classification based on region growing and CNN segmentation. Expert Syst Appl 42(3):990–1002

    Article  Google Scholar 

  80. Rampun A, Morrow PJ, Scotney BW, Winder J (2017) Fully automated breast boundary and pectoral muscle segmentation in mammograms. Artif Intell Med 79:28–41

    Article  Google Scholar 

  81. Ribli D, Horváth A, Unger Z, Pollner P, Csabai I (2018) Detecting and classifying lesions in mammograms with Deep Learning, Sci Rep 8(1)

  82. Kermani BG, White MW, Nagle HT (1995) Feature extraction by genetic algorithms for neural networks in breast cancer classification. In: Proceedings of the 17th annual conference on IEEE engineering in medicine and biology society (pp. 8311–832), vol 831

  83. Verma B, Zhang P (2007) A novel neural-genetic algorithm to find the most significant combination of features in digital mammograms. Appl Soft Comput 7(2):612–625

    Article  Google Scholar 

  84. Abbass HA (2002) An evolutionary ANNs approach for breast cancer diagnosis. Artif Intell Med 25(3):265–281

    Article  Google Scholar 

  85. Dhanya R, Paul IR, Akula SS, Sivakumar M, Nair JJ (2020) F-test FS in Stacking ensemble model for breast cancer prediction. Proced Comput Sci 171:1561–1570

    Article  Google Scholar 

  86. Supriya M, Deepa AJ (2019) A novel approach for breast cancer prediction using optimized ANN classifier based on big data environment. Health care Manage Sci, 1-13

  87. https://archive.ics.uci.edu/ml/datasets/breast+cancer+wisconsin+(original)

  88. https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic)

  89. http://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Prognostic)

  90. Rebecca Sawyer Lee, Francisco Gimenez, Assaf Hoogi , Daniel Rubin (2016). Curated Breast Imaging Subset of DDSM . The Cancer Imaging Archive.

  91. Suckling J, Parker J, Dance D, Astley S, Hutt I, Boggis C, Ricketts I et al (2015) MIAS (MIAS) database v1.21 . https://www.repository.cam.ac.uk/handle/1810/250394

  92. http://medicalresearch.inescportopt/breastresearch/GetINbreastDatabase.html

  93. Siddavaatam P, Sedaghat R (2020) A novel multi-objective optimizer framework for TDMA-based medium access control in IoT. CSIT 8:319–330. https://doi.org/10.1007/s40012-020-00283-7

    Article  Google Scholar 

  94. 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. Biocyber Biomed Eng 40(1):337–351

    Google Scholar 

  95. Derangula A, Edara SR (2021) Identification of optimized features using nature-inspired meta-herustics based optimizations in breast cancer detection. In: Materials Today: Proceedings

  96. Nayak M, Das S, Bhanja U, Senapati MR (2020) Elephant herding optimization technique based neural network for cancer prediction. Inf Med Unlock 21:100445

    Article  Google Scholar 

  97. Ghanem WAHM, Jantan A (2018) A cognitively inspired hybridization of artificial bee colony and dragonfly algorithms for training multi-layer perceptrons. Cogn Comput 10(6):1096–1134

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Karunya Institute of Technology and Sciences, Coimbatore, India

    Punitha Stephan

  2. Department of Computer Science and Engineering, Faculty of Engineering and Technology, M. S. Ramaiah University of Applied Sciences, Bangalore, Karnataka, India

    Thompson Stephan

  3. Department of Electrical and Electronics Engineering, Universiti Teknologi PETRONAS, Seri Iskandar, Malaysia

    Ramani Kannan

  4. Machine Intelligence Research Labs (MIR Labs), Scientific Network for Innovation and Research Excellence, Washington, USA

    Ajith Abraham

Authors
  1. Punitha Stephan
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Thompson Stephan
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Ramani Kannan
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Ajith Abraham
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Thompson Stephan.

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

Stephan, P., Stephan, T., Kannan, R. et al. A hybrid artificial bee colony with whale optimization algorithm for improved breast cancer diagnosis. Neural Comput & Applic 33, 13667–13691 (2021). https://doi.org/10.1007/s00521-021-05997-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-021-05997-6

Keywords