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Comparative Study Using Weka for Red Blood Cells Classification

Authors: Jameela Ali Alkrimi, Hamid A. Jalab, Loay E. George, Abdul Rahim Ahmad, Azizah Suliman, Karim Al-Jashamy


Red blood cells (RBC) are the most common types of blood cells and are the most intensively studied in cell biology. The lack of RBCs is a condition in which the amount of hemoglobin level is lower than normal and is referred to as “anemia”. Abnormalities in RBCs will affect the exchange of oxygen. This paper presents a comparative study for various techniques for classifying the RBCs as normal or abnormal (anemic) using WEKA. WEKA is an open source consists of different machine learning algorithms for data mining applications. The algorithms tested are Radial Basis Function neural network, Support vector machine, and K-Nearest Neighbors algorithm. Two sets of combined features were utilized for classification of blood cells images. The first set, exclusively consist of geometrical features, was used to identify whether the tested blood cell has a spherical shape or non-spherical cells. While the second set, consist mainly of textural features was used to recognize the types of the spherical cells. We have provided an evaluation based on applying these classification methods to our RBCs image dataset which were obtained from Serdang Hospital - Malaysia, and measuring the accuracy of test results. The best achieved classification rates are 97%, 98%, and 79% for Support vector machines, Radial Basis Function neural network, and K-Nearest Neighbors algorithm respectively.

Keywords: K-Nearest Neighbors, Neural Network, Radial Basis Function, Red blood cells, Support vector machine.

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[1] K. Parmar, M. Patel, and P. Chauhan, "A Review on Anaemia. Pharmacie Globale, 2011, 11(02), pp1-6.
[2] W. H. Organization, Global health risks: mortality and burden of disease attributable to selected major risks: World Health Organization, 2009.
[3] R. NH Nik, "The Rate and Risk Factors for Anemia among Pregnant Mothers in Jerteh Terengganu, Malaysia," Journal of Community Medicine & Health Education, 2012, Volume 2 • Issue 5,pp 1-4.
[4] S. L. Perkins, "Pediatric red cell disorders and pure red cell aplasia," Am J ClinPathol, vol. 122, pp. S70-S86, 2004.
[5] Emedicine.medscape.(2009).Available:
[6] D. Frejlichowski, "Pre-processing, extraction and recognition of binary erythrocyte shapes for computer-assisted diagnosis based on MGG images," in Computer Vision and Graphics, ed: Springer, 2010, pp. 368- 375.
[7] Z. Yılmaz and M. R. Bozkurt, "Determination of Women Iron Deficiency Anemia Using Neural Networks," Journal of medical systems, vol. 36, pp. 2941-2945, 2012.
[8] A. Kratz, H.-I. Bengtsson, J. E. Casey, J. M. Keefe, G. H. Beatrice, D. Y. Grzybek, et al., "Performance Evaluation of the CellaVisionDM96 System WBC Differentials by Automated Digital Image Analysis Supported by an Artificial Neural Network," American journal of clinical pathology, vol. 124, pp. 770-781, 2005.
[9] P. M. Barnaghi, V. A. Sahzabi, and A. A. Bakar, "A Comparative Study for Various Methods of Classification," International Proceedings of Computer Science & Information Technology, vol. 27, 2012.
[10] M. F. bin Othman and T. M. S. Yau, "Comparison of different classification techniques using WEKA for breast cancer," in 3rd Kuala Lumpur International Conference on Biomedical Engineering 2006, 2007, pp. 520-523.
[11] W. Ismail, R. Hassan, A. Payne, and S. Swift, "The detection and classification of blast cell in Leukaemia Acute PromyelocyticLeukaemia (AML M3) blood using simulated annealing and neural networks," 2011.
[12] S. Lee, M. S. Cho, K. Jung, and J. H. Kim, "Scene text extraction with edge constraint and text collinearity," in Pattern Recognition (ICPR), 2010 20th International Conference on, 2010, pp. 3983-3986.
[13] S. A. Naji, R. Zainuddin, and H. A. Jalab, "Skin segmentation based on multi pixel color clustering models," Digital Signal Processing, vol. 22, pp. 933-940, 2012.
[14] WEKA at
[15] S. Savkare and S. Narote, "Automatic System for Classification of Erythrocytes Infected with Malaria and Identification of Parasite's Life Stage," Procedia Technology, vol. 6, pp. 405-410, 2012.
[16] C. Li, S. Zhang, H. Zhang, L. Pang, K. Lam, C. Hui, et al., "Using the K-Nearest Neighbor Algorithm for the Classification of Lymph Node Metastasis in Gastric Cancer," Computational and mathematical methods in medicine, vol. 2012, 2012.
[17] P. Rajendran and M. Madheswaran, "An improved brain image classification technique with mining and shape prior segmentation procedure," Journal of medical systems, vol. 36, pp. 747-764, 2012.