Skip to main content
SpringerLink
Log in

Automatic acute lymphoblastic leukemia classification model using social spider optimization algorithm

  • Methodologies and Application
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

The main purpose of this paper is to identify and segment each white blood cells (WBCs) from microscopic images and then classify it to affected or non-affected by acute lymphoblastic leukemia (ALL). The proposed model started by, firstly, detection and isolation of (WBCs). This was performed by conversion of cell images from RGB to CMYK color space. Then histogram equalization followed by thresholding estimation by Zack algorithm was performed for segmentation of cells from the surrounding blood contents. Secondly, some features were extracted from the segmented cells, and they included color, shape, texture and hybrid features. Thirdly, social spider optimization algorithm (SSOA) was applied to select the most appropriate features. Finally, several classifiers were used to validate the performance of the proposed algorithms. The proposed model was applied to the well-known ALL-IDB2 dataset. The results show: firstly, the segmentation (identification and isolations) results were 99.23, 100 and 97.1% of segmentation accuracy, sensitivity and specificity, respectively, which is the highest among other published papers. Secondly, the classification accuracy of the proposed model was higher than the non-SSOA in most experiments. The proposed system achieved remarkable results; 95.23% of classification accuracy with a feature reduction ratio of about 50%, which is the highest among the most recent works on the same dataset.

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
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  • Abdeldaim AM, Sahlol AT, Elhoseny M, Hassanien AE (2017) Computer aided acute lymphoblastic leukemia diagnosis system based on machine learning, advances in soft computing and machine learning in image processing. Springer, Berlin

    Google Scholar 

  • Basha SH, Sahlol AT, El Baz SM, Hassanien AE (2017) Neutrosophic rule-based prediction system for assessment of trace metal pollution on Benthic foraminifera in Burullus Lagoon in Egypt. In: The 12th IEEE international conference on computer engineering and systems (ICCES 2017), Egypt, December 19–20, 2017

  • Bennett JM, Catovsky D, Daniel MT, Flandrin G, Galton DA, Gralnick HR et al (1976) Proposals for the classification of the acute leukemias. French–American–British (FAB) co-operative group. Br J Hematol 33:451–458

    Article  Google Scholar 

  • Bhattacharjee R, Saini LM (2015) Robust technique for the detection of acute lymphoblastic leukemia. In: IEEE power, communication and information technology conference (PCITC), Bhubaneswar, pp 657–662

  • Bhattacharya A, Chattopadhyay PK (2010) Biogeography-based optimization for different economic load dispatch problems. IEEE Trans Power Syst 25:1064–1077

    Article  Google Scholar 

  • Biondi A, Cimino G, Pieters R, Pui CH (2000) Biological and therapeutic aspects of infant leukemia. Blood 96(1):24–33

    Google Scholar 

  • Deore SG, Nemade N (2013) Image analysis framework for automatic extraction of the progress of an infection. Int J Adv Res Comput Sci Softw Eng 3:703–707

    Google Scholar 

  • El-Bages MS, Elsayed WT (2017) Social spider algorithm for solving the transmission expansion planning problem. Electr Power Syst Res 143:235–243

    Article  Google Scholar 

  • El-Fergany AA, Elhameed MA (2016) Efficient frequency controllers for autonomous two-area hybrid microgrid system using social-spider optimiser. IET Gener Transm Distrib (in press)

  • Ewees AA, Sahlol AT, Amasha MA (2017) A Bio-inspired moth-flame optimization algorithm for Arabic handwritten letter recognition. In: 2017 international conference on control, artificial intelligence, robotics and optimization (ICCAIRO), Prague, pp 154–159

  • Inaba H, Greaves M, Mullighan CG (2013) Acute lymphoblastic leukaemia. Lancet 381:1943–1955

    Article  Google Scholar 

  • James JQ, Li VO (2014) Base station switching problem for green cellular networks with social spider algorithm. In: IEEE congress on evolutionary computation (CEC), pp 1–7

  • James JQ, Li VO (2015) A social spider algorithm for global optimization. Appl Soft Comput 30:614–627

    Article  Google Scholar 

  • Kang F, Han S, Salgado R, Li J (2015) System probabilistic stability analysis of soil slopes using Gaussian process regression with Latin hypercube sampling. Comput Geotech 63:13–25

    Article  Google Scholar 

  • Kang F, Xu Q, Li JJ (2016) Slope reliability analysis using surrogate models via new support vector machines with swarm intelligence. Appl Math Model 40(11–12):6105–6120

    Article  MathSciNet  Google Scholar 

  • Labati RD, Piuri V, Scotti F (2011) ALL-IDB: the acute lymphoblastic leukemia image data base for image processing. In: The 18th IEEE international conference on image processing (ICIP), pp 2045–2048

  • Madhukar M, Again S, Chronopoulos AT (2012) New decision support tool for acute lymphoblastic leukemia classification. In: IS and T/SPIE electronic imaging. International Society for Optics and Photonics

  • Mohammed R, Nomir O, Khalifa I (2014) Segmentation of acute lymphoblastic leukemia using \(C\)–\(Y\) color space. Int J Adv Comput Sci Appl 5(11):99–101

    Google Scholar 

  • Putzu L, Caocci G, Di Ruberto C (2014) Leucocyte classification for leukaemia detection using image processing techniques. Artif Intell Med 62:179–191

    Article  Google Scholar 

  • Sahlol AT, Suen CY, Elbasyoni MR, Sallam AA (2014) Investigating of preprocessing techniques and novel features in recognition of handwritten Arabic characters. In: El Gayar N, Schwenker F, Suen C (eds) Artificial neural networks in pattern recognition. Springer, Cham, pp 264–276

    Google Scholar 

  • Sahlol AT, Elfattah MA, Suen CY, Hassanien AE (2016a) Particle swarm optimization with random forests for handwritten Arabic recognition system. In: Proceedings of the international conference on advanced intelligent systems and informatics (AISI 2016), Cairo–Egypt, pp 437–446

  • Sahlol AT, Hemdan AM, Ewees AA, Hassanien AE (2016b) Training feed forward neural networks using sine–cosine algorithm to improve the prediction of liver enzymes based on nano-selenium. In: 2016 IEEE ICEC, 12th International Computer Engineering Conference, Egypt, 22–24 December, 2016

  • Sahlol AT, Hemdan AM, Hassanien AE (2016c) Prediction of antioxidant status in fish farmed on selenium nanoparticles using neural network regression algorithm. In: Proceedings of the international conference on advanced intelligent systems and informatics (AISI 2016), Cairo–Egypt, 24–26 November, pp 353–364

  • Sahlol AT, Suen CY, Zawbaa HM, Hassanien AE, Elfattah MA (2016d) Bio-inspired BAT optimization technique for handwritten Arabic characters recognition. In: IEEE congress on evolutionary computation (WCCI-2016), Vancouver, Canada, pp 1749–1756

  • Sahlol AT, Hassanien AE (2017) Bio-inspired optimization algorithms for Arabic handwritten characters. In: Hassanien AE (ed) Handbook of research on machine learning innovations and trends. IGI Global, Hershey

    Google Scholar 

  • Sahlol AT, Elhoseny M, Elhariri E, Hassanien AE (2017a) Arabic handwritten characters recognition system, towards improving its accuracy. In: 2017 IEEE international conference on intelligent techniques in control, optimization and signal processing, Kalasalingam University-India, IEEE, March 23– 25, 2017

  • Sahlol AT, Ismail FH, Abdeldaim A, Hassanien AE (2017b) Elephant herd optimization with neural networks: a case study on acute lymphoblastic leukemia diagnosis. In: The 12th IEEE international conference on computer engineering and systems (ICCES 2017), Egypt, December 19–20

  • Sahlol AT, Moemen YS, Ewees AA, Hassanien AE (2017c) Evaluation of cisplatin efficiency as a chemotherapeutic drug based on neural networks optimized by genetic algorithm. In: The 12th IEEE international conference on computer engineering and systems (ICCES 2017), Egypt, December 19–20, 2017

  • Singhal V, Singh P (2014) Local binary pattern for detection of acute lymphoblastic leukemia. In: National conference on communication (NCC), pp 1–5

  • Singhal V, Singh P (2016) Texture features for the detection of acute lymphoblastic leukemia. In: Proceedings of international conference on ICT for sustainable development. Springer, Singapore, pp 535–543

  • Soille P (1999) Morphological image analysis: principles and applications. Springer, Berlin, pp 170–171

    Book  MATH  Google Scholar 

  • Zack G, Rogers W, Latt S (1977) Automatic measurement of sister chromatid exchange frequency. J Histochem Cytochem 25(7):741–753

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Damietta University, Damietta, Egypt

    Ahmed T. Sahlol

  2. 6 October University, Cairo, Egypt

    Ahmed M. Abdeldaim

  3. Cairo University, Cairo, Egypt

    Aboul Ella Hassanien

  4. Scientific Research Group in Egypt (SRGE), Cairo, Egypt

    Ahmed M. Abdeldaim & Aboul Ella Hassanien

Authors
  1. Ahmed T. Sahlol
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ahmed M. Abdeldaim
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Aboul Ella Hassanien
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ahmed T. Sahlol.

Ethics declarations

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 or animals performed by any of the authors.

Additional information

Communicated by V. Loia.

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

Sahlol, A.T., Abdeldaim, A.M. & Hassanien, A.E. Automatic acute lymphoblastic leukemia classification model using social spider optimization algorithm. Soft Comput 23, 6345–6360 (2019). https://doi.org/10.1007/s00500-018-3288-5

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-018-3288-5

Keywords

Search

Navigation