Computer-aided classification of images containing white blood cells

Document Type : Research Paper


1 Department of Information System Engineering, Erbil Technical Engineering College, Erbil Polytechnic University, Kirkuk Road, Erbil, Iraq

2 Department of Information Technology, College of Engineering and Computer Science, Lebanese French University, Erbil, Iraq


The counts of various types of white blood cells offer important information that can be used in the diagnostic process for a wide variety of disorders. The automation of this procedure allows for time savings and eliminates the possibility of counting mistakes. In this study, the authors make an attempt to categorize the white blood cells that are found in the peripheral blood based on the shapes of the nuclei and the characteristics that they exhibit. The authors put in place a system and make use of it to automatically identify and analyze White Blood Cells (WBCs). A blood cell can be segmented, scanned, have its features extracted, and then be classified using the system that was proposed. These are the four processes that make up the process. To begin, the authors used segmenting the cell images, which involves grouping white blood cells into their respective clusters. The second part of the process consists in scanning each image that has been segmented and producing the dataset. The third phase involves the form and texture of an image that has been scanned. In the final stage, the authors apply various machine-learning techniques to classify the outcome based on these criteria. These methods include Naïve Bayes, Random Tree, and K-star.


[1] O.M. Amin Ali, S. Wahhab Kareem, and A.S. Mohammed, Evaluation of electrocardiogram signals classification using CNN, SVM, and LSTM algorithm: A review, 8th Int. Engin. Conf. Sustain. Technol. Dev. (IEC) (Erbil, Iraq), IEEE, February 2022, pp. 185–191.
[2] H.Q. Awla, A. Rahman Mirza, and S.W. Kareem, An automated CAPTCHA for website protection based on user behavioral model, 8th Int. Engin. Conf. Sustain. Technol. Dev. (IEC) (Erbil, Iraq), IEEE, February 2022, pp. 161–167.
[3] S.K. Bandyopadhyay, Method for Blood cell segmentation, J. Global Res. Comput. Sci. 2 (2011), no. 4, 130–135.
[4] S. Banerjee, B.R. Ghosh, S. Giri, and D. Ghosh, Automated system for detection of white blood cells in human blood sample, Smart Computing and Informatics, Springer, 2018, pp. 13–20.
[5] R.R. Bouckaert, E. Frank, M. Hall, R. Kirkby, P. Reutemann, A. Seewald, and D. Scuse, WEKA manual for version 3-7-8 2013, Available at:. Accessed July 21 (2013).
[6] L. Breiman, Bagging predictors (technical report 421), University of California, Berkeley, 1994.
[7] T. Das, Machine learning algorithms for image classification of hand digits and face recognition dataset, Machine Learn. 4 (2017), no. 12, 640–649.
[8] T.M. Deserno, Fundamentals of biomedical image processing, Biomedical Image Processing, Springer, 2010, pp. 1– 51.
[9] A. Gautam and H. Bhadauria, Classification of white blood cells based on morphological features, IEEE, 2014, pp. 2363–2368.
[10] R.S. Hawezi, F.S. Khoshaba, and S.W. Kareem, A comparison of automated classification techniques for image processing in video internet of things, Comput. Electric. Engin. 101 (2022), 108074.
[11] M.D. Joshi, A.H. Karode, and S.R. Suralkar, White blood cells segmentation and classification to detect acute leukemia, Int. J. Emerg. Trends Technol. Comput. Sci. 2 (2013), 147–151.
[12] F. Kamiran and T. Calders, Data preprocessing techniques for classification without discrimination, Knowledge Inf. Syst. 33 (2012), no. 1, 1–33.
[13] S.W. Kareem, An evaluation algorithms for classifying leukocytes images, IEEE, 2021, pp. 67–72.
[14] M. Kuhn and K. Johnson, Applied predictive modeling, vol. 26, Springer, 2013.
[15] S. Loussaief and A. Abdelkrim, Machine learning framework for image classification, IEEE, 2016, pp. 58–61.
[16] K. M-Amen, O. Abdullah, A. Amin, Z. Mohamed, B. Hasan, M. Shekha, H. Najmuldeen, F. Rahman, Z. Housein, A. Salih, A. Mohammed, L. Sulaiman, B. Barzingi, D. Mahmood, H. Othman, D. Mohammad, F. Salih, S. Ali, T. Mohamad, K. Mahmood, G. Othman, M. Aali, G. Qader, B. Hussen, F. Awla, S. Kareem, F. Qadir, D. Taher, and A. Salihi, Cancer incidence in the kurdistan region of Iraq: Results of a seven-year cancer registration in Erbil and Duhok governorates, Asian Pacific J. Cancer Prevent. 23 (2022), no. 2, 601–615 (en).
[17] D.Y. Mahmood and M.A. Hussein, Intrusion detection system based on K-star classifier and feature set reduction, IOSR J. Comput. Engin. 15 (2013), no. 5, 107–12.
[18] H.A. Muhamad, Sh.W. Kareem, and A.S. Mohammed, A comparative evaluation of deep learning methods in automated classification of white blood cell images, IEEE, 2022, pp. 205–211.
[19] H.A. Muhamad, S.W. Kareem, and A.S. Mohammed, A deep learning method for detecting leukemia in real images, Neuro Quantol. 20 (2022).
[20] S. Nazlibilek, D. Karacor, K.L. Ert¨urk, G. Sengul, T. Ercan, and F. Aliew, White blood cells classifications by SURF image matching, PCA and dendrogram, Biomed. Res. 26 (2015), no. 4, 633–640.
[21] J. Prinyakupt and C. Pluempitiwiriyawej, Segmentation of white blood cells and comparison of cell morphology by linear and na¨ıve Bayes classifiers, Biomed. Engin. Online 14 (2015), no. 1, 1–19.
[22] F.L. Quilumba, W.-J. Lee, H. Huang, D.Y. Wang, and R. Szabados, An overview of AMI data preprocessing to enhance the performance of load forecasting, IEEE, 2014, pp. 1–7.
[23] R.C. Quinlan, 4.5: Programs for machine learning morgan kaufmann publishers inc, San Francisco, USA, 1993.
[24] P.-N. Tan, M. Steinbach, and V. Kumar, Introduction to data mining, Pearson Education India, 2016.
[25] H. Zhou, J. Wu, and J. Zhang, Digital image processing: part II, Bookboon, 2010.
Volume 14, Issue 1
January 2023
Pages 2223-2231
  • Receive Date: 14 July 2022
  • Revise Date: 19 October 2022
  • Accept Date: 23 November 2022