New Normal and Abnormal Red Blood Cells Features for Improved Classification


  • Jameela Ali Alkrimi College of Dentistry, University of Babylon, Iraq
  • Sherna Aziz Tomeb College of medicine, Baghdad University, Iraq
  • Loay E. Georgec Department of Computer Science, College of Sciences, Baghdad University, Iraq


Principal Component Analysis, RBC classifications, red blood cells features, machine learning algorithms.


This paper focused obtaining new features for improved classification of red blood cells (RBCs). RBCs varies according to shapes, colors and sizes. Abnormal RBCs may be caused by anemia. Abnormal RBCs has great similarities among each other causing difficulties in medical diagnosis. In this work, spatial, spectral statistical features and geometrical features of RBCs are extracted from 1000 normal and abnormal RBCs. The extracted features are reduced using Principal Component Analysis (PCA) and tested with different types of machine learning algorithms for classification. Classifications were evaluated for high sensitivity, specificity, and kappa statistical parameters. The classifications yielded accuracy rates of 97.9%, 98% and 98% for discriminative (SVM), generative (RBFNN) and clustering (K-NN) algorithm respectively, which is an improvement over previous works.


Savkare, S. S., and S. P. Narote. "Blood cell segmentation from microscopic blood images." Information Processing (ICIP), 2015 International Conference on. IEEE, 2015.

Apostolopoulos, G. T. (2010). Recognition and identification of red blood cell size using angular radial transform and neural networks. In XII Mediterranean Conference on Medical and Biological Engineering and Computing 2010 .

J. Ford (2013). Red blood cell morphology, International Journal of Laboratory Hematology, Volume 35, Issue 3,June 2013 ,Pages 351–357.

Jones, K. W. (2009). "Evaluation of cell morphology and introduction to platelet and white blood cell morphology". Clinical Hematology and Fundamentals of Hemostasis , 93-116.

Jameela Ail Alkrimi, L. E.-J. (2014)."Isolation and Classification of Red Blood Cells in Anemic Microscopic Images". World Academy of Science, Engineering and Technology, International Journal of Medical, Health, Biomedical, Bioengineering and Pharmaceutical Engineering 8, no. 10, 8(10), 727-730.

Danglade, F., Veron, P., Pernot, J. P., & Fine, L. (2015). Estimation of CAD model simplification impact on CFD analysis using machine learning techniques.‏

Domingos, P. (2012). A few useful things to know about machine learning. . Communications of the ACM,, 55(10), 78-87.

Elsawy, A. S. (2013). Principal component analysis ensemble classifier for P300 speller applications. 8th International Symposium on In Image and Signal Processing and Analysis (ISPA), 2013, 444-44.

Martín-Fernández, J. A., Pawlowsky-Glahn, V., Egozcue, J. J., & Tolosona-Delgado, R. (2017). Advances in Principal Balances for Compositional Data. Mathematical Geosciences, 1-26.‏

David, Charles C., and Donald J. Jacobs (2014). Principal component analysis: a method for determining the essential dynamics of proteins. In Protein dynamics (pp. 193-226). Humana Press, Totowa, NJ.‏

Matricardi, M. (2010). A principal component based version of the RTTOV fast radiative transfer model. Quarterly Journal of the Royal Meteorological Society, 136(652), 1823-1835.

Nandi, D. A. (2015). Principal component analysis in medical image processing:a study. . International Journal of Image Mining, 1(1), 65-86.

Vincent, I., Shin, B. K., Kwon, S. G., Lee, S. H., & Kwon, K. R. (2014, July). Feature Selection using Principal Component Analysis for Leukemia Classification. In Proceeding of the 10th International Conference on Multimedia Information Technology and Applications 2014 (pp. 206-207).‏

Park, H. S. (2016). Automated Detection of P. falciparum Using Machine Learning Algorithms with Quantitative Phase Images of Unstained Cells. PloS one, 11(9).

Sharma, N. M. (2012). Color image segmentaion techniques and issues: an approach. . International Journal of Scientific & Technology Research, 1(4), 9-12.

Wheeless, L. L. (1994). Classification of red blood cells as normal, sickle, or other abnormal, using a single image analysis feature. Cytometry, 17(2), 159-166.

Henson, R. K., & Roberts, J. K. (2006). Use of exploratory factor analysis in published research common errors and some comment on improved practice.Educational and Psychological measurement, 66(3), 393-416.

Gibson, Ian, and Christopher Amies. "Data normalization techniques." U.S. Patent No. 6,259,456. 10 Jul. 2001.

Abdi, H. (2010). Principal component analysis. Wiley interdisciplinary reviews: computational statistics, 2(4), 433-459.




How to Cite

Ali Alkrimi, J., Aziz Tomeb, S., & E. Georgec, L. (2019). New Normal and Abnormal Red Blood Cells Features for Improved Classification. International Journal of Computer (IJC), 32(1), 1–8. Retrieved from