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Classification of Red Blood Cells using Principal Component Analysis Technique

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  • Jameela Ali Alkrimi
  • Sherna Aziz Tome
  • Loay E. George

Abstract

Principal component analysis (PCA) is based feature reduction that reduces the correlation of features. In this research, a novel approach is proposed by applying the PCA technique on various morphologies of red blood cells (RBCs). According to hematologists, this method successfully classified 40 different types of abnormal RBCs. The classification of RBCs into various distinct subtypes using three machine learning algorithms is important in clinical and laboratory tests for detecting blood diseases. The most common abnormal RBCs are considered as anemic. The RBC features are sufficient to identify the type of anemia and the disease that caused it. Therefore, we found that several features extracted from RBCs in the blood smear images are not significant for classification when observed independently but are significant when combined with other features. The number of feature vectors is reduced from 271 to 8 as time resuming in training and accuracy percentage increased to 98%.

Suggested Citation

  • Jameela Ali Alkrimi & Sherna Aziz Tome & Loay E. George, 2019. "Classification of Red Blood Cells using Principal Component Analysis Technique," European Journal of Engineering and Technology Research, European Open Science, vol. 4(2), pages 17-22, February.
    • Handle: RePEc:epw:ejeng0:v:4:y:2019:i:2:id:61007
    DOI: 10.24018/ejeng.2019.4.2.1007
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    References listed on IDEAS

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    1. Han Sang Park & Matthew T Rinehart & Katelyn A Walzer & Jen-Tsan Ashley Chi & Adam Wax, 2016. "Automated Detection of P. falciparum Using Machine Learning Algorithms with Quantitative Phase Images of Unstained Cells," PLOS ONE, Public Library of Science, vol. 11(9), pages 1-19, September.
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