Comparative Study Using Weka for Red Blood Cells Classification

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.

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

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

[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:
http://emedicine.medscape.com/article/206107-overview
[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 http://www.cs.waikato.ac.nz/~ml/weka.
[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.
[18] http://www.dmi.columbia.edu/homepages/chuangj/kappa.