scholarly journals Implementation of Convolutional Neural Network in the classification of red blood cells have affected of malaria

SinkrOn ◽  
2020 ◽  
Vol 5 (2) ◽  
pp. 199-207
Author(s):  
Mawaddah Harahap ◽  
Jefferson Jefferson ◽  
Surya Barti ◽  
Suprianto Samosir ◽  
Christi Andika Turnip
Keyword(s):  
Neural Network ◽  
Blood Cell ◽  
Convolutional Neural Network ◽  
Red Blood Cells ◽  
Red Blood Cell ◽  
Blood Cells ◽  
Training Data ◽  
Test Accuracy ◽  
Number Of Layers

Malaria is a disease caused by plasmodium which attacks red blood cells. Diagnosis of malaria can be made by examining the patient's red blood cells using a microscope. Convolutional Neural Network (CNN) is a deep learning method that is growing rapidly. CNN is often used in image classification. The CNN process usually requires considerable resources. This is one of the weaknesses of CNN. In this study, the CNN architecture used in the classification of red blood cell images is LeNet-5 and DRNet. The data used is a segmented image of red blood cells and is secondary data. Before conducting the data training, data pre-processing and data augmentation from the dataset was carried out. The number of layers of the LeNet-5 and DRNet models were 4 and 7. The test accuracy of the LeNet-5 and DrNet models was 95% and 97.3%, respectively. From the test results, it was found that the LeNet-5 model was more suitable in terms of red blood cell classification. By using the LeNet-5 architecture, the resources used to perform classification can be reduced compared to previous studies where the accuracy obtained is also the same because the number of layers is less, which is only 4 layers

scholarly journals A deep convolutional neural network for classification of red blood cells in sickle cell anemia

2017 ◽  
Vol 13 (10) ◽  
pp. e1005746 ◽  
Author(s):  
Mengjia Xu ◽  
Dimitrios P. Papageorgiou ◽  
Sabia Z. Abidi ◽  
Ming Dao ◽  
Hong Zhao ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Red Blood Cells ◽  
Sickle Cell ◽  
Sickle Cell Anemia ◽  
Blood Cells ◽  
Deep Convolutional Neural Network

Microfluidic electrical impedance assessment of red blood cell mediated microvascular occlusion

Lab on a Chip ◽  
2021 ◽  
Author(s):  
Yuncheng Man ◽  
Debnath Maji ◽  
Ran An ◽  
Sanjay Ahuja ◽  
Jane A Little ◽  
...  
Keyword(s):  
Sickle Cell Disease ◽  
Blood Cell ◽  
Red Blood Cells ◽  
Sickle Cell ◽  
Red Blood Cell ◽  
Blood Cells ◽  
Electrical Impedance ◽  
Cell Disease ◽  
Blood Disorders

Alterations in the deformability of red blood cells (RBCs), occurring in hemolytic blood disorders such as sickle cell disease (SCD), contributes to vaso-occlusion and disease pathophysiology. However, there are few...

Effect of red blood cells and red blood cell ghosts on reticuloendothelial system function

1984 ◽  
Vol 41 (1) ◽  
pp. 67-73 ◽  
Author(s):  
Daniel J. Loegering ◽  
Gary J. Grover ◽  
Marlowe J. Schneidkraut
Keyword(s):  
Blood Cell ◽  
Red Blood Cells ◽  
Red Blood Cell ◽  
Blood Cells ◽  
Reticuloendothelial System ◽  
System Function

Permeation of the luminal capillary glycocalyx is determined by hyaluronan

1999 ◽  
Vol 277 (2) ◽  
pp. H508-H514 ◽  
Author(s):  
Charmaine B. S. Henry ◽  
Brian R. Duling
Keyword(s):  
Blood Cell ◽  
Red Blood Cells ◽  
Red Blood Cell ◽  
Blood Cells ◽  
Enzyme Treatment ◽  
Apical Surface ◽  
Bright Field ◽  
Endothelial Surface ◽  
The Difference

The endothelial cell glycocalyx influences blood flow and presents a selective barrier to movement of macromolecules from plasma to the endothelial surface. In the hamster cremaster microcirculation, FITC-labeled Dextran 70 and larger molecules are excluded from a region extending almost 0.5 μm from the endothelial surface into the lumen. Red blood cells under normal flow conditions are excluded from a region extending even farther into the lumen. Examination of cultured endothelial cells has shown that the glycocalyx contains hyaluronan, a glycosaminoglycan which is known to create matrices with molecular sieving properties. To test the hypothesis that hyaluronan might be involved in establishing the permeation properties of the apical surface glycocalyx in vivo, hamster microvessels in the cremaster muscle were visualized using video microscopy. After infusion of one of several FITC-dextrans (70, 145, 580, and 2,000 kDa) via a femoral cannula, microvessels were observed with bright-field and fluorescence microscopy to obtain estimates of the anatomic diameters and the widths of fluorescent dextran columns and of red blood cell columns (means ± SE). The widths of the red blood cell and dextran exclusion zones were calculated as one-half the difference between the bright-field anatomic diameter and the width of the red blood cell column or dextran column. After 1 h of treatment with active Streptomyces hyaluronidase, there was a significant increase in access of 70- and 145-kDa FITC-dextrans to the space bounded by the apical glycocalyx, but no increase in access of the red blood cells or in the anatomic diameter in capillaries, arterioles, and venules. Hyaluronidase had no effect on access of FITC-Dextrans 580 and 2,000. Infusion of a mixture of hyaluronan and chondroitin sulfate after enzyme treatment reconstituted the glycocalyx, although treatment with either molecule separately had no effect. These results suggest that cell surface hyaluronan plays a role in regulating or establishing permeation of the apical glycocalyx to macromolecules. This finding and our prior observations suggest that hyaluronan and other glycoconjugates are required for assembly of the matrix on the endothelial surface. We hypothesize that hyaluronidase creates a more open matrix, enabling smaller dextran molecules to penetrate deeper into the glycocalyx.

Susceptibility of density-fractionated erythrocytes to subhaemolytic mechanical shear stress

2018 ◽  
Vol 42 (3) ◽  
pp. 151-157 ◽  
Author(s):  
Antony P McNamee ◽  
Kieran Richardson ◽  
Jarod Horobin ◽  
Lennart Kuck ◽  
Michael J Simmonds
Keyword(s):  
Blood Cell ◽  
Red Blood Cells ◽  
Red Blood Cell ◽  
Blood Cells ◽  
Shear Stresses ◽  
Mechanical Stimuli ◽  
Cell Deformability ◽  
Red Blood Cell Deformability ◽  
Cell Age ◽  
Low Shear

Introduction: Accumulating evidence demonstrates that subhaemolytic mechanical stresses, typical of circulatory support, induce physical and biochemical changes to red blood cells. It remains unclear, however, whether cell age affects susceptibility to these mechanical forces. This study thus examined the sensitivity of density-fractionated red blood cells to sublethal mechanical stresses. Methods: Red blood cells were isolated and washed twice, with the least and most dense fractions being obtained following centrifugation (1500 g × 5 min). Red blood cell deformability was determined across an osmotic gradient and a range of shear stresses (0.3–50 Pa). Cell deformability was also quantified before and after 300 s exposure to shear stresses known to decrease (64 Pa) or increase (10 Pa) red blood cell deformability. The time course of accumulated sublethal damage that occurred during exposure to 64 Pa was also examined. Results: Dense red blood cells exhibited decreased capacity to deform when compared with less dense cells. Cellular response to mechanical stimuli was similar in trend for all red blood cells, independent of density; however, the magnitude of impairment in cell deformability was exacerbated in dense cells. Moreover, the rate of impairment in cellular deformability, induced by 64 Pa, was more rapid for dense cells. Relative improvement in red blood cell deformability, due to low-shear conditioning (10 Pa), was consistent for both cell populations. Conclusion: Red blood cell populations respond differently to mechanical stimuli: older (more dense) cells are highly susceptible to sublethal mechanical trauma, while cell age (density) does not appear to alter the magnitude of improved cell deformability following low-shear conditioning.

scholarly journals Small Red Blood Cell Fraction on the UF-1000i Urine Analyzer as a Screening Tool to Detect Dysmorphic Red Blood Cells for Diagnosing Glomerulonephritis

2019 ◽  
Vol 39 (3) ◽  
pp. 271
Author(s):  
Hyunjung Kim ◽  
Young Ok Kim ◽  
Yonggoo Kim ◽  
Jin-Soon Suh ◽  
Eun-Jung Cho ◽  
...  
Keyword(s):  
Blood Cell ◽  
Red Blood Cells ◽  
Red Blood Cell ◽  
Blood Cells ◽  
Screening Tool ◽  
Cell Fraction
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