Image classification of unlabeled malaria parasites in red blood cells

2016 ◽  
Author(s):  
Zheng Zhang ◽  
L.L. Sharon Ong ◽  
Kong Fang ◽  
Athul Matthew ◽  
Justin Dauwels ◽  
...  
Keyword(s):  
Red Blood Cells ◽  
Image Classification ◽  
Blood Cells ◽  
Malaria Parasites

Image Classification of Abnormal Red Blood Cells Using Decision Tree Algorithm

2020 ◽  
Author(s):  
Virgnia Mari E. Batitis ◽  
Merwin Jhan G. Caballes ◽  
Abigail A. Ciudad ◽  
Miccaela D. Diaz ◽  
Russel D. Flores ◽  
...  
Keyword(s):  
Decision Tree ◽  
Red Blood Cells ◽  
Image Classification ◽  
Blood Cells ◽  
Decision Tree Algorithm ◽  
Tree Algorithm

STUDIES ON THE BIOLOGICAL PROPERTIES AND CLASSIFICATION OF SV4 VIRUS

1965 ◽  
Vol 11 (2) ◽  
pp. 325-335 ◽  
Author(s):  
S. A. Sattar ◽  
K. R. Rozee
Keyword(s):  
Electron Microscope ◽  
Rhesus Monkey ◽  
Red Blood Cells ◽  
Acridine Orange ◽  
Blood Cells ◽  
Magnesium Chloride ◽  
Fluorescent Microscopy ◽  
Biological Properties ◽  
Acridine Orange Staining

Cytopathic changes in LLC-MK2 cells infected with SV4 virus, observed with the electron microscope and using acridine orange staining and fluorescent microscopy, have been shown to be similar to that caused by picornaviruses and members of the Columbia-SK virus group. The virus was found to be stabilized against heat in the presence of molar magnesium chloride, and to be stable at pH 3.5. The virus was non-pathogenic for suckling mice, failed to agglutinate sheep and human "O" red blood cells, but agglutinated rhesus monkey erythrocytes at 4 °C. On the basis of these properties and those already known, it was suggested that SV4 virus be placed in the group Enteroviruses of lower animals.

scholarly journals OVERVIEW OF MALARIA PARASITE AND ITS PREVENTION IN INDIA

2019 ◽  
Vol 8 (1) ◽  
Author(s):  
Adil Raza ◽  
Megha Chaudhary ◽  
Sonika Devi
Keyword(s):  
Red Blood Cells ◽  
Peripheral Blood ◽  
Blood Cells ◽  
Malaria Parasite ◽  
Plasmodium Species ◽  
Peripheral Blood Smear ◽  
Infected Mosquito ◽  
Protozoan Parasites ◽  
Malaria Parasites ◽  
Genus Plasmodium

Background: Malaria is a systematic disease caused by a parasite called Plasmodium which is transmitted into the human blood via female Anopheles mosquito. Malaria in humans is caused by four species of protozoan parasites of the genus Plasmodium: P. falciparum, P. vivax, P. ovale, and P. malariae. The parasite enters the human body through a mosquito bite and travel to the very crucial organ, the liver, where they multiply and come back to the bloodstream and destroy red blood cells. Malaria causes symptoms that typically include fever, tiredness, vomiting, and headaches. In severe cases it can cause yellow skin, seizures, coma, or death. Symptoms usually begin ten to fifteen days after being bitten by an infected mosquito. In those who have recently survived an infection, reinfection usually causes milder symptoms. Objectives: Isolation of different species of malaria parasites. The prevalence of malaria parasite in India. Methods: The procedure follows these steps: collection of peripheral blood, staining of smear with Leishman’s stain and examination of red blood cells for malaria parasites under the microscope. Results: We observed the plasmodium species in peripheral blood smear. Conclusion: Worldwide, the number of cases of malaria caused by Plasmodium falciparum, the most dangerous species of the parasite, is on the rise.

scholarly journals Invariant Classification of Red Blood Cells

2001 ◽  
pp. 367-371 ◽  
Author(s):  
Daniel Keysers ◽  
Jörg Dahmen ◽  
Hermann Ney
Keyword(s):  
Red Blood Cells ◽  
Blood Cells

scholarly journals Deep Learning Models for Classification of Red Blood Cells in Microscopy Images to Aid in Sickle Cell Anemia Diagnosis

Electronics ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 427 ◽  
Author(s):  
Laith Alzubaidi ◽  
Mohammed A. Fadhel ◽  
Omran Al-Shamma ◽  
Jinglan Zhang ◽  
Ye Duan
Keyword(s):  
Deep Learning ◽  
Red Blood Cells ◽  
Transfer Learning ◽  
Sickle Cell ◽  
Sickle Cell Anemia ◽  
Blood Cells ◽  
Learning Models ◽  
Sickle Cells ◽  
Microscopy Images

Sickle cell anemia, which is also called sickle cell disease (SCD), is a hematological disorder that causes occlusion in blood vessels, leading to hurtful episodes and even death. The key function of red blood cells (erythrocytes) is to supply all the parts of the human body with oxygen. Red blood cells (RBCs) form a crescent or sickle shape when sickle cell anemia affects them. This abnormal shape makes it difficult for sickle cells to move through the bloodstream, hence decreasing the oxygen flow. The precise classification of RBCs is the first step toward accurate diagnosis, which aids in evaluating the danger level of sickle cell anemia. The manual classification methods of erythrocytes require immense time, and it is possible that errors may be made throughout the classification stage. Traditional computer-aided techniques, which have been employed for erythrocyte classification, are based on handcrafted features techniques, and their performance relies on the selected features. They also are very sensitive to different sizes, colors, and complex shapes. However, microscopy images of erythrocytes are very complex in shape with different sizes. To this end, this research proposes lightweight deep learning models that classify the erythrocytes into three classes: circular (normal), elongated (sickle cells), and other blood content. These models are different in the number of layers and learnable filters. The available datasets of red blood cells with sickle cell disease are very small for training deep learning models. Therefore, addressing the lack of training data is the main aim of this paper. To tackle this issue and optimize the performance, the transfer learning technique is utilized. Transfer learning does not significantly affect performance on medical image tasks when the source domain is completely different from the target domain. In some cases, it can degrade the performance. Hence, we have applied the same domain transfer learning, unlike other methods that used the ImageNet dataset for transfer learning. To minimize the overfitting effect, we have utilized several data augmentation techniques. Our model obtained state-of-the-art performance and outperformed the latest methods by achieving an accuracy of 99.54% with our model and 99.98% with our model plus a multiclass SVM classifier on the erythrocytesIDB dataset and 98.87% on the collected dataset.

scholarly journals Integrating deep learning with microfluidics for biophysical classification of sickle red blood cells adhered to laminin

2021 ◽  
Vol 17 (11) ◽  
pp. e1008946
Author(s):  
Niksa Praljak ◽  
Shamreen Iram ◽  
Utku Goreke ◽  
Gundeep Singh ◽  
Ailis Hill ◽  
...  
Keyword(s):  
Image Analysis ◽  
Red Blood Cells ◽  
Blood Cells ◽  
Automated Image Analysis ◽  
Cell Counting ◽  
Data Set ◽  
Analysis Workflow ◽  
Sickle Red Blood Cells ◽  
Validation Set

Sickle cell disease, a genetic disorder affecting a sizeable global demographic, manifests in sickle red blood cells (sRBCs) with altered shape and biomechanics. sRBCs show heightened adhesive interactions with inflamed endothelium, triggering painful vascular occlusion events. Numerous studies employ microfluidic-assay-based monitoring tools to quantify characteristics of adhered sRBCs from high resolution channel images. The current image analysis workflow relies on detailed morphological characterization and cell counting by a specially trained worker. This is time and labor intensive, and prone to user bias artifacts. Here we establish a morphology based classification scheme to identify two naturally arising sRBC subpopulations—deformable and non-deformable sRBCs—utilizing novel visual markers that link to underlying cell biomechanical properties and hold promise for clinically relevant insights. We then set up a standardized, reproducible, and fully automated image analysis workflow designed to carry out this classification. This relies on a two part deep neural network architecture that works in tandem for segmentation of channel images and classification of adhered cells into subtypes. Network training utilized an extensive data set of images generated by the SCD BioChip, a microfluidic assay which injects clinical whole blood samples into protein-functionalized microchannels, mimicking physiological conditions in the microvasculature. Here we carried out the assay with the sub-endothelial protein laminin. The machine learning approach segmented the resulting channel images with 99.1±0.3% mean IoU on the validation set across 5 k-folds, classified detected sRBCs with 96.0±0.3% mean accuracy on the validation set across 5 k-folds, and matched trained personnel in overall characterization of whole channel images with R2 = 0.992, 0.987 and 0.834 for total, deformable and non-deformable sRBC counts respectively. Average analysis time per channel image was also improved by two orders of magnitude (∼ 2 minutes vs ∼ 2-3 hours) over manual characterization. Finally, the network results show an order of magnitude less variance in counts on repeat trials than humans. This kind of standardization is a prerequisite for the viability of any diagnostic technology, making our system suitable for affordable and high throughput disease monitoring.

scholarly journals How Malaria Parasites Reduce the Deformability of Infected Red Blood Cells

2012 ◽  
Vol 103 (1) ◽  
pp. 1-10 ◽  
Author(s):  
S. Majid Hosseini ◽  
James J. Feng
Keyword(s):  
Red Blood Cells ◽  
Blood Cells ◽  
Malaria Parasites
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