Classification of Red Blood Cells in Sickle Cell Anemia Using Deep Convolutional Neural Network

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


Sickle cell anemia is an abnormal red blood cell which leads to blood vessel obstruction joined by painful episodes and even death. It is also called abnormal hemoglobin. Hemoglobin is responsible for passing oxygen through the blood vessel for all over the body. Normal red blood cells are in a circular shape and they are compact and flexible, enabling them to move freely through small capillaries. On the other hand, abnormal red blood cells are in sickle shape and they are stiff and angular causing them to become stuck in small capillaries. Due to that, it will be a reason for pain to patients and lead to low oxygen and dehydration. The manual assessment, classification, and counting of biological cells require for an immense spending of time and it may lead to wrong classification and counting since red blood cells are millions in one smear. Also, cells classification is challenging due to heterogeneous and complex shapes, overlapped cells and a variety of colors. We overcome these drawbacks by introducing a new robust and effective deep Convolutional Neural Network to classify Red Blood Cells (RBCs) in three classes namely: normal (‘N’) abnormal (sickle cells anemia type (‘S’)) and miscellaneous (‘M’). In order to improve the results further, we have used our model as features extractor then we applied an error-correcting output codes (ECOC) classifier for the classification task. Our model with ECOC showed outstanding performance and high accuracy of 92.06%.


Classification Convolutional Neural Network Cells counting Sickle Cell Anemia (SCA) Red blood cells ECOC classifier 


  1. 1.
    Anglin, C.: Sickle cell disease. J. Consum. Health Internet 19(2), 122–131 (2015)CrossRefGoogle Scholar
  2. 2.
    Fasano, R.M., Booth, G.S., Miles, M., Du, L., Koyama, T., Meier, E.R., et al.: Red blood cell alloimmunization is influenced by recipient inflammatory state at time of transfusion in patients with sickle cell disease. Br. J. Haematol. 168(2), 291–300 (2015)CrossRefGoogle Scholar
  3. 3.
    Abubakar, I., Tillmann, T., Banerjee, A.: Global, regional, and national age-sex specific all-cause and cause-specific mortality for 240 causes of death, 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet 385(9963), 117–171 (2015)CrossRefGoogle Scholar
  4. 4.
    Milton, J.N., Gordeuk, V.R., Taylor, J.G., Gladwin, M.T., Steinberg, M.H., Sebastiani, P.: Prediction of fetal hemoglobin in sickle cell anemia using an ensemble of genetic risk prediction models. Circ. Cardiovasc. Genet. 7(2), 110–115 (2014). Scholar
  5. 5.
    Darrow, M.C., Zhang, Y., Cinquin, B.P., Smith, E.A., Boudreau, R., Rochat, R.H., et al.: Visualizing red blood cell sickling and the effects of inhibition of sphingosine kinase 1 using soft X-ray tomography. J. Cell Sci. 129(18), 3511–3517 (2016)CrossRefGoogle Scholar
  6. 6.
    Van Beers, E.J., Samsel, L., Mendelsohn, L., Saiyed, R., Fertrin, K.Y., Brantner, C.A., et al.: Imaging flow cytometry for automated detection of hypoxia-induced erythrocyte shape change in sickle cell disease. Am. J. Hematol. 89(6), 598–603 (2014)CrossRefGoogle Scholar
  7. 7.
    Araújo, T., et al.: Classification of breast cancer histology images using convolutional neural networks. PloS One 12(6), e0177544 (2017)CrossRefGoogle Scholar
  8. 8.
    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
  9. 9.
    Albehadili, H., et al.: Fast and accurate real time pedestrian detection using convolutional neural network. In: The 1 st International Conference on Information Technology (ICoIT 2017) (2017)Google Scholar
  10. 10.
    Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: European Conference on Computer Vision. Springer, Cham (2014)Google Scholar
  11. 11.
    LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)CrossRefGoogle Scholar
  12. 12.
    Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)Google Scholar
  13. 13.
    Arenas, J.O.P., Moreno, R.J., Beleño, R.D.H.: Convolutional neural network with a DAG architecture for control of a robotic arm by means of hand gestures. Contemp. Eng. Sci. 11(12), 547–557 (2018)CrossRefGoogle Scholar
  14. 14.
    Zhou, J., et al.: On applicability of auxiliary system approach to detect generalized synchronization in complex network. IEEE Trans. Autom. Control 62(7), 3468–3473 (2017)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Ye, Q., Liang, J., Jiao, J.: Pedestrian detection in video images via error correcting output code classification of manifold subclasses. IEEE Trans. Intell. Transp. Syst. 13(1), 193–202 (2012)CrossRefGoogle Scholar
  16. 16.
  17. 17. Accessed 1 Sept 2018
  18. 18.
    Litjens, G., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60–88 (2017)CrossRefGoogle Scholar
  19. 19.
    Vedaldi, A., Lenc, K.: MatConvNet: convolutional neural networks for MATLAB. In: ACM International Conference on Multimedia, pp. 689–692. ACM (2015)Google Scholar
  20. 20.
    Weatherall, D.J.: The importance of micro mapping the gene frequencies for the common inherited disorders of haemoglobin. Br. J. Haematol. 149, 635–637 (2010)CrossRefGoogle Scholar
  21. 21.
    Marsh, V., Kombe, F., Fitzpatrick, R., Williams, T.N., Parker, M., Molyneux, S.: Consulting communities on feedback of genetic findings in international health research: sharing sickle cell disease and carrier information in coastal Kenya. BMC Med. Ethics 14, 41 (2013)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Laith Alzubaidi
    • 1
    • 2
  • Omran Al-Shamma
    • 2
  • Mohammed A. Fadhel
    • 2
    Email author
  • Laith Farhan
    • 3
    • 4
  • Jinglan Zhang
    • 1
  1. 1.Faculty of Science and EngineeringQueensland University of TechnologyBrisbaneAustralia
  2. 2.University of Information Technology and CommunicationsBaghdadIraq
  3. 3.School of EngineeringManchester Metropolitan UniversityManchesterUK
  4. 4.University of DiyalaBaqubahIraq

Personalised recommendations