Robust and Efficient Approach to Diagnose Sickle Cell Anemia in Blood

  • Laith Alzubaidi
  • Mohammed A. FadhelEmail author
  • Omran Al-Shamma
  • Jinglan Zhang
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 940)


Red Blood Corpuscles (RBCs) form the main cellular component of human blood. RBCs in common physiological status has a circular form in the front view and bi-concave form in side view. In case of a person is infected with anemia RBCs form as sickle-shaped cells which drive to blood vessel obstruction joined by painful episodes and even death. Precise and robust cells classification and counting are essential in evaluating the level of anemia disease danger. The classification and counting of red blood cells (normal and abnormal RBC cells) are challenging due to the complex and heterogeneous shapes and overlapped cells. In this paper, we propose a new robust approach to classify red blood cells to two groups: Normal and Abnormal RBC cells based on area and Eccentricity of each cell, then count the total number of normal and abnormal RBC cells individually. For the sake of comparison, we also implement the latest Sickle cell research, which uses circular Hough transform. We compare our approach to circular Hough transform in the same execution environment. Our new approach sets the state-of-the-art performance in term of effectiveness (cell counting) and efficiency (execution time).


Cell counting Sickle Cell Anemia (SCA) Circular Hough transform Eccentricity Area Classification 


  1. 1.
    Vos, T., et al.: Global, regional, and national incidence, prevalence, and years lived with disability for 301 acute and chronic diseases and injuries in 188 countries, 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet 386(9995), 743–800 (2015)CrossRefGoogle Scholar
  2. 2.
    Kuchana, V., Kumar, P., Anjum, M.: Int. J. Med. Nanotechnol.Google Scholar
  3. 3.
    Mahmood, N.H., Lim, P.C.: Blood cells extraction using color based segmentation technique. Int. J. Life Sci. Biotechnol. Pharma Res. 2(2), 233–240 (2013)Google Scholar
  4. 4.
    Adewoyin, A.S.: Management of sickle cell disease: a review for physician education in Nigeria (sub-saharan Africa). Anemia 2015, 791498 (2015)CrossRefGoogle Scholar
  5. 5.
    Alzubaidi, L., et al.: Nucleus detection in H&E images with fully convolutional regression networks. In: Proceedings of the First International Workshop on Deep Learning for Pattern Recognition (2016)Google Scholar
  6. 6.
    Rakshit, P., Bhowmik, K.: Detection of abnormal finding in human RBC in diagnosing sickle cell anaemia using image processing. In: International Conference on Computational Intelligence: Modeling, Technique and Application, pp. 28–36 (2013)CrossRefGoogle Scholar
  7. 7.
    Gonzalez, R.G., Woods, R.E., Eddins, S.L.: Digital Image Processing. Pearson Education, Inc., Upper Saddle River (2007)Google Scholar
  8. 8.
    Chintawar, I.A., et al.: Detection of sickle cells using image processing. Int. J. Sci. Technol. Eng. 2(9), 335–339 (2016)Google Scholar
  9. 9.
    Taherisadr, M., et al.: New approach to red blood cell classification using morphological image processing. Shiraz E-Medical J. 14(1), 44–53 (2013)Google Scholar
  10. 10.
    Veluchamy, M., Perumal, K., Ponuchamy, T.: Feature extraction and classification of blood cells using artificial neural network. Am. J. Appl. Sci. 9(5), 615 (2012)CrossRefGoogle Scholar
  11. 11.
    Barpanda, S.S.: Sekhar: use of image processing techniques to automatically diagnose sickle-cell anaemia present in RBC smear. National Institute of Technology (ODISHA) (2013)Google Scholar
  12. 12.
    Memeu, D.M.: A rapid malaria diagnostic method based on automatic detection and classification of plasmodium parasites in stained thin blood smear images. University of Nairobi (2014)Google Scholar
  13. 13.
    AbdulraheemFadhel, M., Humaidi, A.J., RazzaqOleiwi, S.: Image processing-based diagnosis of sickle cell anemia in erythrocytes. In: 2017 Annual Conference on New Trends in Information & Communications Technology Applications (NTICT). IEEE (2017)Google Scholar
  14. 14. Accessed 1 Sept 2018
  15. 15.
    Xu, M., et al.: A deep convolutional neural network for classification of red blood cells in sickle cell anemia. PLoS Comput. Biol. 13(10), e1005746 (2017)CrossRefGoogle Scholar
  16. 16.
    Khalaf, M., et al.: The utilisation of composite machine learning models for the classification of medical datasets for sickle cell disease. In: 2016 Sixth International Conference on Digital Information Processing and Communications (ICDIPC). IEEE (2016)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Laith Alzubaidi
    • 1
    • 2
  • Mohammed A. Fadhel
    • 2
    Email author
  • Omran Al-Shamma
    • 2
  • Jinglan Zhang
    • 1
  1. 1.Faculty of Science and EngineeringQueensland University of TechnologyBrisbaneAustralia
  2. 2.University of Information Technology and CommunicationsBaghdadIraq

Personalised recommendations