Abstract
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%.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Anglin, C.: Sickle cell disease. J. Consum. Health Internet 19(2), 122–131 (2015)
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)
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)
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). https://doi.org/10.1161/CIRCGENETICS.113.000387
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)
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)
Araújo, T., et al.: Classification of breast cancer histology images using convolutional neural networks. PloS One 12(6), e0177544 (2017)
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)
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)
Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: European Conference on Computer Vision. Springer, Cham (2014)
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)
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)
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)
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)
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)
https://www.nhlbi.nih.gov/health-topics/sickle-cell-disease. Accessed 1 Sept 2018
http://sicklecellanaemia.org/. Accessed 1 Sept 2018
Litjens, G., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60–88 (2017)
Vedaldi, A., Lenc, K.: MatConvNet: convolutional neural networks for MATLAB. In: ACM International Conference on Multimedia, pp. 689–692. ACM (2015)
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)
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Alzubaidi, L., Al-Shamma, O., Fadhel, M.A., Farhan, L., Zhang, J. (2020). Classification of Red Blood Cells in Sickle Cell Anemia Using Deep Convolutional Neural Network. In: Abraham, A., Cherukuri, A.K., Melin, P., Gandhi, N. (eds) Intelligent Systems Design and Applications. ISDA 2018 2018. Advances in Intelligent Systems and Computing, vol 940. Springer, Cham. https://doi.org/10.1007/978-3-030-16657-1_51
Download citation
DOI: https://doi.org/10.1007/978-3-030-16657-1_51
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-16656-4
Online ISBN: 978-3-030-16657-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)