Advertisement

Rough set theory with Jaya optimization for acute lymphoblastic leukemia classification

  • G. Jothi
  • H. Hannah Inbarani
  • Ahmad Taher Azar
  • K. Renuga Devi
Original Article

Abstract

Early diagnosis of malignant leukemia can enormously help the physicians in choosing the right treatment for the patient. A lot of diagnostic techniques are available to identify leukemia disease, but these techniques are costly. Hence, there is a need for a less time-consuming and cost-effective method for the classification of leukemia blood cells. In this paper, application of graphical user interface technique for the differentiation of acute lymphoblastic leukemia nucleus from healthy lymphocytes in a medical image is described. This method employs backtrack search optimization algorithm for clustering. Five different categories of features are extracted from the segmented nucleus images, i.e., morphological, wavelet, color, texture and statistical features. Feature selection plays a very important role in medical image processing. It reduces the computational time and memory space. The hybrid intelligent framework includes the benefits of the basic models; and in the meantime, it overcomes their limitations. Three different kinds of hybrid supervised feature selection algorithms such as tolerance rough set particle swarm optimization-based quick reduct, tolerance rough set particle swarm optimization-based relative reduct and tolerance rough set firefly-based quick reduct are applied for selecting prominent features. These algorithms incorporate the strengths of evolutionary algorithms. The redundant features are eliminated to generate the reduced set which gives predictive capability equal to that of the original set of features. Jaya algorithm is applied for optimizing the rules generated from classification algorithms. Classification algorithms such as Naïve Bayes, linear discriminant analysis, K-nearest neighbor, support vector machine, decision tree and ensemble random undersampling boost are applied on leukemia dataset. Experimental results depict that the above classification algorithms after optimizing with Jaya algorithm improve classification accuracy compared to the results obtained before optimizing with Jaya algorithm.

Keywords

Acute lymphoblastic leukemia BSA clustering Segmentation Supervised feature selection Firefly algorithm Classification Jaya algorithm Tolerance rough set Rough set theory 

Notes

Compliance with ethical standards

Conflict of interest

All authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants performed by any of the authors.

References

  1. 1.
    Wahhab HTA (2015) Classification of acute leukemia using image processing and machine learning techniques. Thesis, University of Malaya, Kuala LumpurGoogle Scholar
  2. 2.
  3. 3.
    Civicioglu P (2013) Backtracking search optimization algorithm for numerical optimization problems. Appl Math Comput 219:8121–8144MathSciNetMATHGoogle Scholar
  4. 4.
    Pawlak Z (1982) Rough sets. Int J Inf Comput Sci 11(5):341–356CrossRefMATHGoogle Scholar
  5. 5.
    Parthaláin NM, Shen Q (2009) Exploring the boundary region of tolerance rough sets for feature selection. Pattern Recognit 42:655–667CrossRefMATHGoogle Scholar
  6. 6.
    Yang X (2009) Firefly algorithm for multimodal optimization. In: SAGA, lecture notes in computer science, pp 169–178Google Scholar
  7. 7.
    Zang et al (2010) A review of nature inspired algorithm. J Bionic Eng 7:232–237CrossRefGoogle Scholar
  8. 8.
    Inbarani H Hannah, Azar Ahmad Taher, Jothi G (2014) Supervised hybrid feature selection based on PSO and rough sets for medical diagnosis. Comput Methods Programs Biomed 113(1):175–185CrossRefGoogle Scholar
  9. 9.
    Ganesan J, Inbarani HH, Azar AT, Polat K (2016) Tolerance rough set firefly-based quick reduct. Neural Comput Appl 28(10):1–14Google Scholar
  10. 10.
    Azar AT, Kumar SS, Inbarani HH, Hassanien AE (2016) Pessimistic multi-granulation rough set based classification for heart valve disease diagnosis. Int J Model Identif Control (IJMIC) 26(1):42–51CrossRefGoogle Scholar
  11. 11.
    Azar AT, Inbarani HH, Devi KR (2016) Improved dominance rough set-based classification system. Neural Comput Appl.  https://doi.org/10.1007/s00521-016-2177-z Google Scholar
  12. 12.
    Kumar SS, Inbarani HH, Azar AT, Polatn(2016) Covering rough set based classification system. Neural Comput Appl.  https://doi.org/10.1007/s00521-016-2412-7 Google Scholar
  13. 13.
    Senthil Kumar S, Hannah Inbarani H, Azar AT, Own HS, Balas VE, Olariu T (2016) Optimistic multi-granulation rough set-based classification for neonatal jaundice diagnosis. In: Balas V, C Jain L, Kovačević B (eds) Soft computing applications. Advances in intelligent systems and computing, vol 356. Springer, Cham.  https://doi.org/10.1007/978-3-319-18296-4_26
  14. 14.
    Inbarani HH, Kumar SU, Azar AT, Hassanien AE (2016) Hybrid rough-bijective soft set classification system. Neural Comput Appl.  https://doi.org/10.1007/s00521-016-2711-z Google Scholar
  15. 15.
    Inbarani HH, Bagyamathi M, Azar AT (2015) A novel hybrid feature selection method based on rough set and improved harmony search. Neural Comput Appl.  https://doi.org/10.1007/s00521-015-1840-0 Google Scholar
  16. 16.
    Kumar SS, Inbarani HH, Azar AT, Hassanien AE (2015) Rough set based meta-heuristic clustering approach for social E-learning systems. Int J Intell Eng Inform 3(1):23–41Google Scholar
  17. 17.
    Azar AT, Vashist R, Vashishtha A (2015) A rough set based total quality management approach in higher education. In: Zhu Q, Azar AT (eds) Complex system modelling and control through intelligent soft computations, studies in fuzziness and soft computing, vol 319. Springer, Germany, pp 389–406.  https://doi.org/10.1007/978-3-319-12883-2_14 Google Scholar
  18. 18.
    Azar AT, Bouaynaya N, Polikar R (2015) Inductive learning based on rough set theory for medical decision making. In: 2015 IEEE international conference on fuzzy systems (FUZZ-IEEE), vol 2–5, pp 1–8.  https://doi.org/10.1109/fuzz-ieee.2015.7338075
  19. 19.
    Azar AT (2014) Neuro-fuzzy feature selection approach based on linguistic hedges for medical diagnosis. Int J Model Identif Control (IJMIC) 22(3):195–206.  https://doi.org/10.1504/ijmic.2014.065338 CrossRefGoogle Scholar
  20. 20.
    Azar AT, Hassanien AE (2014) Dimensionality reduction of medical big data using neural-fuzzy classifier. Soft Comput 19(4):1115–1127.  https://doi.org/10.1007/s00500-014-1327-4 CrossRefGoogle Scholar
  21. 21.
    Inbarani HH, Kumar SS, Azar AT, Hassanien AE (2014) Soft rough sets for heart valve disease diagnosis. In: Hassanien AE, Tolba M, Azar AT (eds) Advanced machine learning technologies and applications: second international conference, AMLTA 2014, Cairo, Egypt, November 28–30. Proceedings, communications in computer and information science, vol 488. Springer, Berlin. ISBN: 978-3-319-13460-4Google Scholar
  22. 22.
    Elshazly HI, Azar AT, Elkorany AM, Hassanien AE (2013) Hybrid system based on rough sets and genetic algorithms for medical data classifications. Int J Fuzzy Syst Appl (IJFSA) 3(4):31–46CrossRefGoogle Scholar
  23. 23.
    Azar AT (2013) Fast neural network learning algorithms for medical applications. Neural Comput Appl 23(3–4):1019–1034.  https://doi.org/10.1007/s00521-012-1026-y CrossRefGoogle Scholar
  24. 24.
    Azar AT, El-Said SA (2013) Probabilistic neural network for breast cancer classification. Neural Comput Appl 23(6):1737–1751.  https://doi.org/10.1007/s00521-012-1134-8 CrossRefGoogle Scholar
  25. 25.
    Azar AT, El-Metwally SM (2013) Decision tree classifiers for automated medical diagnosis. Neural Comput Appl 23(7–8):2387–2403.  https://doi.org/10.1007/s00521-012-1196-7 CrossRefGoogle Scholar
  26. 26.
    Azar AT, El-Said SA (2013) Superior neuro-fuzzy classification systems. Neural Comput Appl 23(1):55–72.  https://doi.org/10.1007/s00521-012-1231-8 CrossRefGoogle Scholar
  27. 27.
    Hassanien AE, Azar AT, Snasel V, Kacprzyk J, Abawajy JH (2015) Big data in complex systems: challenges and opportunities, studies in big data, vol 9. Springer, Berlin. ISBN 978-3-319-11055-4Google Scholar
  28. 28.
    Zhu Q, Azar AT (2015) Complex system modelling and control through intelligent soft computations. Studies in fuzziness and soft computing, vol 319. Springer, Germany. ISBN: 978-3-319-12882-5Google Scholar
  29. 29.
    Azar AT, Vaidyanathan S (2015) Computational Intelligence applications in modeling and control. Studies in computational intelligence, vol 575. Springer, Germany. ISBN 978-3-319-11016-5Google Scholar
  30. 30.
    Roy P, Goswami S, Chakraborty S, Azar AT, Dey N (2014) Image segmentation using rough set theory: a review. Int J Rough Sets Data Anal 1(2):62–74CrossRefGoogle Scholar
  31. 31.
    Chowdhuri S, Roy P, Goswami S, Azar AT, Dey N (2014) Rough set based ad hoc network: a review. Int J Serv Sci Manag Eng Technol 5(4):66–76CrossRefGoogle Scholar
  32. 32.
    Azar AT, El-Said SA (2014) Performance analysis of support vector machines classifiers in breast cancer mammography recognition. Neural Comput Appl 24(5):163–1177.  https://doi.org/10.1007/s00521-012-1324-4 CrossRefGoogle Scholar
  33. 33.
    Moftah HM, Azar AT, Al-Shammari ET, Ghali NI, Hassanien AE, Shoman M (2014) Adaptive k-means clustering algorithm for MR breast image segmentation. Neural Comput Appl 24(7–8):1917–1928.  https://doi.org/10.1007/s00521-013-1437-4 CrossRefGoogle Scholar
  34. 34.
    Banu PKN, Inbarani HH, Azar AT, Hala S, Own HS, Hassanien AE (2014) Rough set based feature selection for egyptian neonatal jaundice. In: Hassanien AE, Tolba M, Azar AT (eds) Advanced machine learning technologies and applications: second international conference, AMLTA 2014, Cairo, Egypt, November 28–30, 2014. Proceedings, communications in computer and information science, vol 488. Springer, Berlin. ISBN: 978-3-319-13460-4Google Scholar
  35. 35.
    Hassanien AE, Tolba M, Azar AT (2014) Advanced machine learning technologies and applications: second international conference, AMLTA 2014, Cairo, Egypt, November 28–30, 2014. Proceedings, communications in computer and information science, vol 488. Springer, Berlin. ISBN: 978-3-319-13460-4Google Scholar
  36. 36.
    Jiang K, Liao QM, Dai SY (2003) A novel white blood cell segmentation scheme using scale-space filtering and watershed clustering. In: 2003 international conference on machine learning and cybernetics, vol 5. IEEE, pp 2820–2825Google Scholar
  37. 37.
    Escalante HJ et al (2012) Acute leukemia classification by ensemble particle swarm model selection. Artif Intell Med 55(3):163–175CrossRefGoogle Scholar
  38. 38.
    Putzu L, Di Ruberto C (2013) White blood cells identification and classification from leukemic blood image. In: Proceedings of the IWBBIO international work-conference on bioinformatics and biomedical engineeringGoogle Scholar
  39. 39.
    Dhanachandra N, Manglem K, Chanu YJ (2015) Image segmentation using K-means clustering algorithm and subtractive clustering algorithm. Procedia Comput Sci 54:764–771CrossRefGoogle Scholar
  40. 40.
    Rawat J et al (2015) Computer aided diagnostic system for detection of leukemia using microscopic images. Procedia Comput Sci 70:748–756CrossRefGoogle Scholar
  41. 41.
    Agaian S, Madhukar M, Chronopoulos AT (2014) Automated screening system for acute myelogenous leukemia detection in blood microscopic images. Syst J IEEE 8:995–1004CrossRefGoogle Scholar
  42. 42.
    Joshi MD, Karode AH, Suralkar SR (2013) White blood cells segmentation and classification to detect acute leukemia. Int J Emerg Trends Technol Comput Sci (IJETICS) 2(3):147–151Google Scholar
  43. 43.
    Kulkarni-Joshi TA, Bhosale DS (2014) A fast segmentation scheme for acute lymphoblastic leukemia detection. Int J Adv Res Electr Electron Instrum Eng 3(2):7252–7258Google Scholar
  44. 44.
    Carolina R et al (2010) Segmentation of bone marrow cell images for morphological classification of acute leukemia. In: FLAIRS conferenceGoogle Scholar
  45. 45.
    Mohapatra S, Patra D, Satpathy S (2014) An ensemble classifier system for early diagnosis of acute lymphoblastic leukemia in blood microscopic images. Neural Comput Appl 24(7-8):1887–1904CrossRefGoogle Scholar
  46. 46.
    Fatichah C et al (2015) Fuzzy feature representation for white blood cell differential counting in acute leukemia diagnosis. Int J Control Autom Syst 13(3):742–752CrossRefGoogle Scholar
  47. 47.
    Jothi G, Inbarni HH (2016) Hybrid tolerance rough set—firefly based supervised feature selection for MRI brain tumor image classification. Appl Soft Comput 46:639–651CrossRefGoogle Scholar
  48. 48.
    Hassanien AE, Moftah HM, Azar AT, Shoman M (2014) MRI breast cancer diagnosis hybrid approach using adaptive ant-based segmentation and multilayer perceptron neural networks classifier. Appl Soft Comput 14(Part A):62–71CrossRefGoogle Scholar
  49. 49.
    Labati RD, Piuri V, Scotti F (2011) ALL-IDB: the acute lymphoblastic leukemia image database for image processing. In: IEEE international conference on image processing (ICIP), September 11–14Google Scholar
  50. 50.
    Scotti F (2006) Robust segmentation and measurements techniques of white cells in blood microscope images. In: Proceedings of the IEEE instrumentation and measurement technology conference, 2006, pp 43–48Google Scholar
  51. 51.
    Scotti F (2005) Automatic morphological analysis for acute leukemia identification in peripheral blood microscope images. In: IEEE international conference on computational intelligence for measurement systems and applications, pp 96–101Google Scholar
  52. 52.
    Piuri V, Scotti F (2004) Morphological classification of blood leucocytes by microscope images. In: IEEE international conference on computational intelligence for measurement systems and applications, pp 103–108Google Scholar
  53. 53.
    Prabu G, Inbarani HH (2015) PSO for acute lymphoblastic leukemia classification in blood microscopic images. Indian J Eng 12(30):146–151Google Scholar
  54. 54.
    Atasever UH et al (2014) A new unsupervised change detection approach based on DWT image fusion and backtracking search optimization algorithm for optical remote sensing data. Int Arch Photogramm Remote Sens Spat Inf Sci 40:7Google Scholar
  55. 55.
    Putzu L, Caocci G, Di Ruberto C (2014) Leucocyte classification for leukaemia detection using image processing techniques. Artif Intell Med 62(3):179–191CrossRefGoogle Scholar
  56. 56.
    Jothi G, Inbarani HH, Azar AT (2013) Hybrid tolerance-PSO based supervised feature selection for digital mammogram images. Int J Fuzzy Syst Appl 3(4):15–30CrossRefGoogle Scholar
  57. 57.
    Jothi G, Inbarani H (2012) Soft set based unsupervised feature selection for lung cancer images. Int J Sci Res Eng 10:1–7Google Scholar
  58. 58.
    Inbarani HH, Banu PKN (2012) Unsupervised feature selection using tolerance rough set based relative reduct. In: IEEE-international conference on advances in engineering, science and management, pp 326–331Google Scholar
  59. 59.
    Banu PKN, Inbarani HH, Azar AT, Own HS, Hassanien AE (2014) Rough set based feature selection for egyptian neonatal jaundice. In: Hassanien AE, Tolba MF, Taher Azar A (eds) Advanced machine learning technologies and applications. AMLTA 2014. Communications in computer and information science, vol 488. Springer, Cham.  https://doi.org/10.1007/978-3-319-13461-1_35
  60. 60.
    Zhang Y, Yang X, Cattani C, Rao RV, Wang S, Phillips P (2016) Tea category identification using a novel fractional fourier entropy and Jaya algorithm. Entropy 18(77):1–17Google Scholar
  61. 61.
    Kurada RR, Kanadam KP (2016) Automatic unsupervised data classification using Jaya evolutionary algorithm. Adv Comput Intell Int J (ACII) 3(2):35–42CrossRefGoogle Scholar
  62. 62.
    Venkat Rao R (2016) Jaya: a simple and new optimization algorithm for solving constrained and unconstrained optimization problems. Int J Ind Eng Comput 7:19–34Google Scholar
  63. 63.
    Devi KR, Inbarani HH (2016) Motor imagery classification based on variable precision multigranulation rough set and game theoretic rough set. Med Imaging Clin Appl 651:153–174MathSciNetCrossRefGoogle Scholar
  64. 64.
    Basu T, Murthy CA (2012), Effective text classification by a supervised feature selection approach. In: IEEE 12th international conference on data mining workshops (ICDMW), pp 918–925Google Scholar
  65. 65.
    Hollander M, Wolfe DA (1999) Nonparametric statistical methods. Wiley, HobokenMATHGoogle Scholar
  66. 66.
    Hogg RV, Ledolter J (1987) Engineering statistics. Macmillan Pub Co., New YorkGoogle Scholar
  67. 67.
    Fawcett T (2006) An introduction to ROC analysis. Pattern Recognit Lett 27(8):861–874MathSciNetCrossRefGoogle Scholar
  68. 68.
    Patil TR, Sherekar SS (2013) Performance analysis of Naive Bayes and J48 classification algorithm for data classification. Int J Comput Sci Appl 6(2):256–261Google Scholar
  69. 69.
    Vanaja S, Rameshkumar K (2015) Performance analysis of classification algorithms on medical diagnoses—a survey. J Comput Sci 11(1):30–52CrossRefGoogle Scholar

Copyright information

© The Natural Computing Applications Forum 2018

Authors and Affiliations

  • G. Jothi
    • 1
  • H. Hannah Inbarani
    • 2
  • Ahmad Taher Azar
    • 3
    • 4
  • K. Renuga Devi
    • 2
  1. 1.Department of Information TechnologySona College of Technology (Autonomous)SalemIndia
  2. 2.Department of Computer SciencePeriyar UniversitySalemIndia
  3. 3.Faculty of Computers and InformationBenha UniversityBenhaEgypt
  4. 4.School of Engineering and Applied SciencesNile University CampusGizaEgypt

Personalised recommendations