Classification of Anemia Images Based on Principal Component Analysis (PCA)

Authors

  • Asma I. Hussein Department of Computer Science, University of Technology
  • Nidaa F. Hassan Department of Computer Science, University of Technology

DOI:

https://doi.org/10.23851/mjs.v28i1.319

Keywords:

Anemia, Principal Component Analysis (PCA), classification, Decision tree.

Abstract

Blood cells are composed of erythrocytes (Red Blood Cells (RBCs)), the shape of RBC changes when the body suffers from different diseases such as Anemia. Classification of such diseases helps the medical technician to decide the type of Anemia in Laboratory analyzes in the hospitals. This paper proposed an automatic classification algorithm, which discriminates the different types of Anemia using Principal Component Analysis (PCA) algorithm and Decision tree. The proposed algorithm consists of four steps, at the first step preprocessing steps are applied on the RBC image, these RBC images then segmented in the second step, features are extracted using moment invariant in third step, this features are considered input to PCA so as to produced features vectors, at a final step features vector are inputted to Decision Tree to classify RBC image. Best classifications rates are (92%) obtained when using PCA algorithm compared with (74.1 %) which are obtained without applying PCA algorithm.

References

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S. Chandrasiri, et al., "Morphology Based Automatic Disease Analysis Through Evaluation of Red Blood Cells," in Department of Information Technology/ Sri Lanka Institute of Information Technology, 2014 Fifth International Conference on Intelligent Systems, Modelling and Simulation, 2014. CrossRef

M. Hussein, et al.., " Image Authentication Using PCA And BP Neural Network," Eng.& Tech. Journal, vol. 28, no. 22, 2010.

Z..F, "Mammogram Images Classification Using Contourlet Transform," MCS Thesis, University of Technology, Department of Computer Science, 2014.

K.Hosny, "Exact and fast computation of geometric moments for gray level images," Applied Mathematics and Computation Journal, vol. 189, p. 1214–1222, 2007. CrossRef

D. N. George, "Tumor Type Recognition Using Artificial Neural Networks," MSC thesis, Iraqi Commission for Computers And Informatics Institute for Postgraduate Studies, Baghdad, 2013.

R. M, "Classification of Face Image Based on Gender Using Intelligent Method," MCS Thesis, University of Technology, Department of Computer Science, Baghdad, 2015.

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Published

2017-11-19

How to Cite

[1]
A. I. Hussein and N. F. Hassan, “Classification of Anemia Images Based on Principal Component Analysis (PCA)”, MJS, vol. 28, no. 1, pp. 97–102, Nov. 2017.

Issue

Section

Computer Science