scholarly journals Classification of Red Blood Cells using Principal Component Analysis Technique

2019 ◽  
Vol 4 (2) ◽  
pp. 17-22 ◽  
Author(s):  
Jameela Ali Alkrimi ◽  
Sherna Aziz Tome ◽  
Loay E. George
Keyword(s):  
Principal Component Analysis ◽  
Red Blood Cells ◽  
Blood Cells ◽  
Principal Component ◽  
Component Analysis ◽  
Feature Reduction ◽  
Machine Learning Algorithms ◽  
Novel Approach ◽  
Analysis Technique

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%.

scholarly journals THE USE OF PRINCIPAL COMPONENT ANALYSIS FOR EVALUATION OF MORPHOFUNCTIONAL CHANGES IN RED BLOOD CELLS UNDER THEINFLUENCE OF DIFFERENT GLUCOSE CONCE

2020 ◽  
Vol 10 (2) ◽  
pp. 6-10
Author(s):  
Oxana Anfinogenova ◽  
Evgeny Melchenko ◽  
Anna Muratova ◽  
Svetlana Andrusenko Andrusenko ◽  
Ayshat Elkanova ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Red Blood Cells ◽  
Blood Cells ◽  
Principal Component ◽  
Component Analysis

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

2017 ◽  
Vol 28 (1) ◽  
pp. 97
Author(s):  
Asma I. Hussein ◽  
Nidaa F. Hassan
Keyword(s):  
Principal Component Analysis ◽  
Decision Tree ◽  
Blood Cells ◽  
Principal Component ◽  
Component Analysis ◽  
The Body ◽  
Moment Invariant ◽  
Pca Algorithm ◽  
Medical Technician

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.

scholarly journals Denoising and Principal Component Analysis of Amplified Raman Spectra from Red Blood Cells with Added Silver Nanoparticles

2018 ◽  
Vol 2018 ◽  
pp. 1-9
Author(s):  
L. Ferrer-Galindo ◽  
A. D. Sañu-Ginarte ◽  
N. Fleitas-Salazar ◽  
L. A. Ferrer-Moreno ◽  
R. A. Rosas ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Silver Nanoparticles ◽  
Red Blood Cells ◽  
Raman Spectra ◽  
Blood Cells ◽  
Principal Component ◽  
Data Interpretation ◽  
Component Analysis ◽  
Surface Enhanced Raman ◽  
Enhanced Raman Scattering

Incubated erythrocytes with and without silver nanoparticles (AgNP) were analyzed by Raman spectroscopy, resulting in two Raman spectra datasets. AgNP were added to red blood cells (RBC) in order to enhance the Raman signals. This technique is known as surface-enhanced Raman scattering (SERS). A comparison was made between the Raman spectra with and without AgNP, to test if the SERS had taken place. Since Raman and SERS spectra are considered to be cumbersome due to the noises presented, we applied denoising criteria for detection and removal of noises like cosmic rays, shot, and fluorescence contribution. After this, the principal component analysis (PCA) was performed, in order to reduce the dimensions of the spectra being studied. Only the main key components necessary for a better interpretation of these spectra were considered. All of those noises had to be removed prior to the statistical analysis, to make sure the analysis was really based on the Raman measurements and not on other effects. As a result, RBC Raman spectra with and without AgNP got denoised, obtaining an improvement in its resolution for a better signal reading and data interpretation. Also, the first principal components (PC) were selected from each dataset under scrutiny, based on the weight of their information and their spectrum readability. In conclusion, we were able to represent the given reference system with a more affordable and smaller dimension in which information loss was minimal.

Classification of Observations through Combination of the Dimension Reduction and the Cluster Analysis

2017 ◽  
Vol 7 (8) ◽  
pp. 30
Author(s):  
Hyeuk Kim
Keyword(s):  
Machine Learning ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Unsupervised Learning ◽  
Principal Component ◽  
Component Analysis ◽  
Baseball Players ◽  
Partitioning Around Medoids ◽  
Different Characteristics

Unsupervised learning in machine learning divides data into several groups. The observations in the same group have similar characteristics and the observations in the different groups have the different characteristics. In the paper, we classify data by partitioning around medoids which have some advantages over the k-means clustering. We apply it to baseball players in Korea Baseball League. We also apply the principal component analysis to data and draw the graph using two components for axis. We interpret the meaning of the clustering graphically through the procedure. The combination of the partitioning around medoids and the principal component analysis can be used to any other data and the approach makes us to figure out the characteristics easily.

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