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%.
Abdi, H. Principal component analysis. Wiley interdisciplinary reviews: computational statistics, 2(4), 433-459.2010.
Apostolopoulos, G. T. 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.
Dombrowski, S. C. "Exploratory and hierarchical factor analysis of the WJ-IV Cognitive at school age". Psychological assessment 29.4, 394.2017.
Domingos, P. A few useful things to know about machine learning. . Communications of the ACM, 55(10), 78-87. 2012.
Elsawy, A. S. Principal component analysis ensemble classifier for P300 speller applications. 8th International Symposium on In Image and Signal Processing and Analysis (ISPA), , 444-442013.
J. Ail Alkrimi,. "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. 2014.
K. W Jones. "Evaluation of cell morphology and introduction to platelet and white blood cell morphology". Clinical Hematology and Fundamentals of Hemostasis, 93-116. 2009.
Khashman, A. "IBCIS: Intelligent blood cell identification system”. Progress in Natural Science 18.10, 1309-1314. 2008.
Klinkenberg, E. M. Urban malaria and anaemia in children: a cross‐sectional survey in two cities of Ghana. Tropical medicine & international health, 11(5) 578-588. 2006.
Matricardi, M. A principal component based version of the RTTOV fast radiative transfer model. Quarterly Journal of the Royal Meteorological Society, 136(652), 1823-1835. 2010.
Nandi, D. A. Principal component analysis in medical image processing:a study. . International Journal of Image Mining, 1(1), 65-86. 2015
Omer, A. E. "Facial recognition using principal component analysis based dimensionality reduction”. Computing, Control, Networking, Electronics and Embedded Systems Engineering (ICCNEEE), 2015 International Conference on. IEEE. 2015
Park, H. S. Automated Detection of P. Falciparum Using Machine Learning Algorithms with Quantitative Phase Images of Unstained Cells. PloS one, 11(9). 2016.
Nazlibilek, Sedat, Deniz Karacor, Korhan Levent Ertürk, Gokhan Sengul, Tuncay Ercan, and Fuad Aliew. "White blood cells classifications by surf image matching, pca and dendrogram." Biomedical Research 26, no. 4 (2015).
Wheeless, L. L. Classification of red blood cells as normal, sickle, or other abnormal, using a single image analysis feature. Cytometry, 17(2), 159-166. 1994.
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).
Omucheni, D. L., Kaduki, K. A., Bulimo, W. D., & Angeyo, H. K. (2014). Application of principal component analysis to multispectral-multimodal optical image analysis for malaria diagnostics. Malaria journal, 13(1), 485
Jaferzadeh, K., Ahmadzadeh, E., Moon, I., & Gholami, S. (2017, May). Clustering of red blood cells using digital holographic microscopy. In Holography: Advances and Modern Trends V (Vol. 10233, p. 102331E). International Society for Optics and Photonics.
Prinyakupt, J., & Pluempitiwiriyawej, C. (2015). Segmentation of white blood cells and comparison of cell morphology by linear and naïve Bayes classifiers. Biomedical engineering online, 14(1), 63.
Ali, M. U., Ahmed, S., Ferzund, J., Mehmood, A., & Rehman, A. (2017). Using PCA and Factor Analysis for Dimensionality Reduction of Bio-informatics Data. arXiv preprint arXiv:1707.07189.
Jia, W., Chen, P., Chen, W., & Li, Y. (2018). Raman characterizations of red blood cells with β-thalassemia using laser tweezers Raman spectroscopy. Medicine, 97(39)
Sharma, N. M. Color image segmentaion techniques and issues: an approach. International Journal of Scientific & Technology Research, 1(4), 9-12. 2012.
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