Deep Categorization of Blood Cells u sing Depthwise Convolutions

Modern-day computation has become indispensable in the healthcare industry. From medical image processing to cost reduction, Artificial Intelligence has proved its significance in solving complex healthcare problems. One of the primary areas in which it can be of greater use in hematology. Categorization of white-blood cells is imperative to pre-identify abnormalities. Through this paper, we collected image samples for 4 major White Blood cell groups, which are Neutrophils, Lymphocytes, Monocytes, and Eosinophils. The aim of this research is to put forward an intelligent system that efficiently alleviates the stringent requirement of a cytological study. The proposed system classifies 4 white-blood-cell types based on their morphological variation. With the experimental modulations that we chose to integrate, the presented model attained an accuracy of 97%.

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Klasifikasi Sel Darah Putih dan Sel Limfoblas Menggunakan Metode Multilayer Perceptron Backpropagation

IJEIS (Indonesian Journal of Electronics and Instrumentation Systems) ◽

10.22146/ijeis.49943 ◽

2019 ◽

Vol 9 (2) ◽

pp. 173

Author(s):

Apri Nur Liyantoko ◽

Ika Candradewi ◽

Agus Harjoko

Keyword(s):

Blood Cell ◽

White Blood Cell ◽

Blood Cells ◽

White Blood Cells ◽

Back Propagation ◽

Cell Types ◽

Diagnostic Process ◽

The Subject ◽

Processing Techniques

Leukemia is a type of cancer that is on white blood cell. This disease are characterized by abundance of abnormal white blood cell called lymphoblast in the bone marrow. Classification of blood cell types, calculation of the ratio of cell types and comparison with normal blood cells can be the subject of diagnosing this disease. The diagnostic process is carried out manually by hematologists through microscopic image. This method is likely to provide a subjective result and time-consuming.The application of digital image processing techniques and machine learning in the process of classifying white blood cells can provide more objective results. This research used thresholding method as segmentation and multilayer method of back propagation perceptron with variations in the extraction of textural features, geometry, and colors. The results of segmentation testing in this study amounted to 68.70%. Whereas the classification test shows that the combination of feature extraction of GLCM features, geometry features, and color features gives the best results. This test produces an accuration value 91.43%, precision value of 50.63%, sensitivity 56.67%, F1Score 51.95%, and specitifity 94.16%.

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White Blood Cells and Severe COVID-19: A Mendelian Randomization Study

Journal of Personalized Medicine ◽

10.3390/jpm11030195 ◽

2021 ◽

Vol 11 (3) ◽

pp. 195

Author(s):

Yitang Sun ◽

Jingqi Zhou ◽

Kaixiong Ye

Keyword(s):

Blood Cell ◽

Cell Count ◽

White Blood Cell Count ◽

White Blood Cell ◽

Blood Cells ◽

Mendelian Randomization ◽

Association Studies ◽

White Blood Cells ◽

Genome Wide Association Studies ◽

Blood Cell Count

Increasing evidence shows that white blood cells are associated with the risk of coronavirus disease 2019 (COVID-19), but the direction and causality of this association are not clear. To evaluate the causal associations between various white blood cell traits and the COVID-19 susceptibility and severity, we conducted two-sample bidirectional Mendelian Randomization (MR) analyses with summary statistics from the largest and most recent genome-wide association studies. Our MR results indicated causal protective effects of higher basophil count, basophil percentage of white blood cells, and myeloid white blood cell count on severe COVID-19, with odds ratios (OR) per standard deviation increment of 0.75 (95% CI: 0.60–0.95), 0.70 (95% CI: 0.54–0.92), and 0.85 (95% CI: 0.73–0.98), respectively. Neither COVID-19 severity nor susceptibility was associated with white blood cell traits in our reverse MR results. Genetically predicted high basophil count, basophil percentage of white blood cells, and myeloid white blood cell count are associated with a lower risk of developing severe COVID-19. Individuals with a lower genetic capacity for basophils are likely at risk, while enhancing the production of basophils may be an effective therapeutic strategy.

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Differences in DNA methylation of white blood cell types at birth and in adulthood reflect postnatal immune maturation and influence accuracy of cell type prediction

10.1101/399279 ◽

2018 ◽

Cited By ~ 3

Author(s):

Meaghan J Jones ◽

Louie Dinh ◽

Hamid Reza Razzaghian ◽

Olivia de Goede ◽

Julia L MacIsaac ◽

...

Keyword(s):

Dna Methylation ◽

Immune System ◽

Blood Cell ◽

Cord Blood ◽

White Blood Cell ◽

Blood Cells ◽

Cell Types ◽

Cell Type ◽

Cell Type Specific ◽

The Impact

AbstractBackgroundDNA methylation profiling of peripheral blood leukocytes has many research applications, and characterizing the changes in DNA methylation of specific white blood cell types between newborn and adult could add insight into the maturation of the immune system. As a consequence of developmental changes, DNA methylation profiles derived from adult white blood cells are poor references for prediction of cord blood cell types from DNA methylation data. We thus examined cell-type specific differences in DNA methylation in leukocyte subsets between cord and adult blood, and assessed the impact of these differences on prediction of cell types in cord blood.ResultsThough all cell types showed differences between cord and adult blood, some specific patterns stood out that reflected how the immune system changes after birth. In cord blood, lymphoid cells showed less variability than in adult, potentially demonstrating their naïve status. In fact, cord CD4 and CD8 T cells were so similar that genetic effects on DNA methylation were greater than cell type effects in our analysis, and CD8 T cell frequencies remained difficult to predict, even after optimizing the library used for cord blood composition estimation. Myeloid cells showed fewer changes between cord and adult and also less variability, with monocytes showing the fewest sites of DNA methylation change between cord and adult. Finally, including nucleated red blood cells in the reference library was necessary for accurate cell type predictions in cord blood.ConclusionChanges in DNA methylation with age were highly cell type specific, and those differences paralleled what is known about the maturation of the postnatal immune system.

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White Blood Cells Classification with Deep Convolutional Neural Networks

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001418570069 ◽

2018 ◽

Vol 32 (09) ◽

pp. 1857006 ◽

Cited By ~ 6

Author(s):

Ming Jiang ◽

Liu Cheng ◽

Feiwei Qin ◽

Lian Du ◽

Min Zhang

Keyword(s):

Blood Cell ◽

White Blood Cell ◽

Blood Cells ◽

Network Architecture ◽

White Blood Cells ◽

Activation Function ◽

Training Set ◽

Deep Convolutional Neural Networks ◽

Cell Image ◽

Attending Physician

The necessary step in the diagnosis of leukemia by the attending physician is to classify the white blood cells in the bone marrow, which requires the attending physician to have a wealth of clinical experience. Now the deep learning is very suitable for the study of image recognition classification, and the effect is not good enough to directly use some famous convolution neural network (CNN) models, such as AlexNet model, GoogleNet model, and VGGFace model. In this paper, we construct a new CNN model called WBCNet model that can fully extract features of the microscopic white blood cell image by combining batch normalization algorithm, residual convolution architecture, and improved activation function. WBCNet model has 33 layers of network architecture, whose speed has greatly been improved compared with the traditional CNN model in training period, and it can quickly identify the category of white blood cell images. The accuracy rate is 77.65% for Top-1 and 98.65% for Top-5 on the training set, while 83% for Top-1 on the test set. This study can help doctors diagnose leukemia, and reduce misdiagnosis rate.

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A Neural-Network-Based Approach to White Blood Cell Classification

The Scientific World JOURNAL ◽

10.1155/2014/796371 ◽

2014 ◽

Vol 2014 ◽

pp. 1-9 ◽

Cited By ~ 19

Author(s):

Mu-Chun Su ◽

Chun-Yen Cheng ◽

Pa-Chun Wang

Keyword(s):

Blood Cell ◽

White Blood Cell ◽

Classification System ◽

Blood Cells ◽

White Blood Cells ◽

Recognition Rate ◽

Color Space ◽

Segmentation Algorithm ◽

Cell Classification ◽

Morphological Process

This paper presents a new white blood cell classification system for the recognition of five types of white blood cells. We propose a new segmentation algorithm for the segmentation of white blood cells from smear images. The core idea of the proposed segmentation algorithm is to find a discriminating region of white blood cells on the HSI color space. Pixels with color lying in the discriminating region described by an ellipsoidal region will be regarded as the nucleus and granule of cytoplasm of a white blood cell. Then, through a further morphological process, we can segment a white blood cell from a smear image. Three kinds of features (i.e., geometrical features, color features, and LDP-based texture features) are extracted from the segmented cell. These features are fed into three different kinds of neural networks to recognize the types of the white blood cells. To test the effectiveness of the proposed white blood cell classification system, a total of 450 white blood cells images were used. The highest overall correct recognition rate could reach 99.11% correct. Simulation results showed that the proposed white blood cell classification system was very competitive to some existing systems.

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STUDIES ON THE PHYSIOLOGY OF THE WHITE BLOOD CELL

Blood ◽

10.1182/blood.v2.3.235.235 ◽

1947 ◽

Vol 2 (3) ◽

pp. 235-243 ◽

Cited By ~ 22

Author(s):

RICHARD WAGNER

Keyword(s):

Blood Cell ◽

White Blood Cell ◽

Blood Cells ◽

Glycogen Content ◽

Striated Muscle ◽

White Blood Cells ◽

Glycogen Storage ◽

Different Types ◽

Blast Cells ◽

Reducing Substances

Abstract The technic of determining glycogen in isolated white blood cells was applied to the study of the different types of leukemia and of polycythemia, in order to obtain information on the physiology of the white blood cell. From this study it is concluded that the granulated leukocyte is the only carrier of glycogen in whole blood. The "reducing substances" in lymphocytes and blast cells are not considered as true glycogen. The glycogen content of wet white blood cells in the rabbit amounts to about 1 per cent. In the human being a range of from 0.17 to 0.67 per cent was calculated. In disease higher percentages occur, in polycythemia up to 1.64 per cent and in glycogen storage disease up to 3.05 per cent. The glycogen concentration of normal white blood cells is within the same range as that of the striated muscle.

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Detection and Classification of White Blood Cells through Deep Learning Techniques

International Journal of Online and Biomedical Engineering (iJOE) ◽

10.3991/ijoe.v16i15.15481 ◽

2020 ◽

Vol 16 (15) ◽

pp. 94

Author(s):

Samir Abou El-Seoud ◽

Muaad Hammuda Siala ◽

Gerard McKee

Keyword(s):

Neural Network ◽

Blood Cell ◽

Convolutional Neural Network ◽

White Blood Cell ◽

Blood Cells ◽

Human Life ◽

White Blood Cells ◽

Learning Techniques ◽

Normal White Blood Cell ◽

High Level

Leukemia is one of the deadliest diseases in human life, it is a type of cancer that hits blood cells. The task of diagnosing Leukemia is time consuming and tedious for doctors; it is also challenging to determine the level and type of Leukemia. The diagnoses of Leukemia are achieved through identifying the changes on the White blood Cells (WBC). WBCs are divided into five types: Neutrophils, Eosinophils, Basophils, Monocytes, and Lymphocytes. In this paper, the authors propose a Convolutional Neural Network to detect and classify normal white blood cells. The program will learn about the shape and type of normal WBC by performing the following two tasks. The first task is identifying high level features of a normal white blood cell. The second task is classifying the normal white blood cell according to its type. Using a Convolutional Neural Network CNN, the system will be able to detect normal WBCs by comparing them with the high-level features of normal WBC. This process of identifying and classifying WBC can be vital for doctors and medical staff to make a decision. The proposed network achieves an accuracy up to 96.78% with a dataset including 10,000 blood cell images.

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White blood cells and severe COVID-19: a Mendelian randomization study

10.1101/2020.10.14.20212993 ◽

2020 ◽

Author(s):

Yitang Sun ◽

Jingqi Zhou ◽

Kaixiong Ye

Keyword(s):

Blood Cell ◽

Cell Count ◽

White Blood Cell Count ◽

White Blood Cell ◽

Blood Cells ◽

Mendelian Randomization ◽

White Blood Cells ◽

Causal Effects ◽

Blood Cell Count ◽

Granulocyte Count

AbstractBackgroundThe pandemic of coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has rapidly emerged to seriously threaten public health. We aimed to investigate whether white blood cell traits have potential causal effects on severe COVID-19 using Mendelian randomization (MR).MethodsTo evaluate the causal associations between various white blood cell traits and severe COVID-19, we conducted a two-sample MR analysis with summary statistics from recent large genome-wide association studies.ResultsOur MR results indicated potential causal associations of white blood cell count, myeloid white blood cell count, and granulocyte count with severe COVID-19, with odds ratios (OR) of 0.84 (95% CI: 0.72-0.98), 0.81 (95% CI: 0.70-0.94), and 0.84 (95% CI: 0.71-0.99), respectively. Increasing eosinophil percentage of white blood cells was associated with a higher risk of severe COVID-19 (OR: 1.22, 95% CI: 1.03-1.45).ConclusionsOur results suggest the potential causal effects of lower white blood cell count, lower myeloid white blood cell count, lower granulocyte count, and higher eosinophil percentage of white blood cells on an increased risk of severe COVID-19.

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Risk Factors of White Blood Cell Progression Among Patients With Chronic Lymphocytic Leukemia at Felege Hiwot Referral Hospital, Bahir Dar, Ethiopia

Cancer Informatics ◽

10.1177/11769351211069902 ◽

2022 ◽

Vol 21 ◽

pp. 117693512110699

Author(s):

Gedam Derbew Addisia ◽

Awoke Seyoum Tegegne ◽

Denekew Bitew Belay ◽

Mitiku Wale Muluneh ◽

Mahider Abere Kassaw

Keyword(s):

Chronic Lymphocytic Leukemia ◽

Blood Cell ◽

White Blood Cell ◽

Blood Cells ◽

White Blood Cells ◽

Lymphocytic Leukemia ◽

Bahir Dar ◽

Felege Hiwot Referral Hospital ◽

Wbc Count

Background: Leukemia is a type of cancers that start in the bone marrow and produce a serious number of abnormal white blood cells. Bleeding and bruising problems, fatigue, fever, and an increased risk of infection are among symptoms of the disease. The main objective of this study is to identify the determinant of the progression rate of white blood cells among patients with chronic lymphocytic leukemia at Felege Hiwot Referral Hospital (FHRH), Bahir Dar, Ethiopia. Methods: A retrospective study design was conducted on 312 patients with chronic lymphocytic leukemia at FHRH, Bahir Dar, Ethiopia under treatment from 1 January 2017 to 31 December 2019. A linear mixed-effects model was considered for the progression of the white blood cell data. Results: The estimated coefficient of the fixed effect intercept was 84.68, indicating that the average white blood cell (WBC) count of the patients was 84.68 at baseline time by excluding all covariates in the model ( P-value <.001). Male sex ( β = 2.92, 95% confidence interval [CI] 0.58, 0.5.25), age ( β = .17, 95% CI 0.08, 0.28), widowed/divorced marital status ( β = 3.30, 95% CI 0.03, 6.57), medium chronic lymphocytic leukemia (CLL) stage ( β = −4.34, 95% CI −6.57, −2.68), high CLL stage ( β = −2.76, 95% CI −4.86, −0.67), hemoglobin ( β = .15, 95% CI 0.07, 0.22), platelet ( β = .09, 95% CI 0.02, 0.17), lymphocytes ( β = .16, 95% CI 0.03, 0.29), red blood cell (RBC) ( β = .17, 95% CI 0.09, 0.25), and follow-up time ( β = .27, 95% CI 0.19, 0.36) were significantly associated with the average WBC count of chronic lymphocytic leukemia patients. Conclusions: The finding showed that age, sex, lymphocytic, stage of chronic lymphocytic leukemia, marital status, platelet, hemoglobin, RBC, and follow-up time were significantly associated with the average WBC count of chronic lymphocytic leukemia patients. Therefore, health care providers should give due attention and prioritize those identified factors and give frequent counseling about improving the health of chronic lymphocytic leukemia patients.

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Blood

10.1093/med/9780198759782.003.0010 ◽

2018 ◽

Author(s):

Hugh Devlin ◽

Rebecca Craven

Keyword(s):

Blood Transfusion ◽

Blood Cell ◽

Blood Cells ◽

Final Section ◽

Dental Treatment ◽

White Blood Cells ◽

Cell Types ◽

Bleeding Disorders ◽

Blood Tests ◽

Coagulation Pathway

The blood in relation to dentistry is the topic of this chapter. Components of blood are described, including the blood cell types, their development and functions. Anaemias are discussed and their dental implications. Haemostasis and the coagulation pathway are described, followed by the bleeding disorders, how they are detected in blood tests and managed so as to avoid complications arising from dental treatment. The main malignancies of white blood cells are described in relation to dental care. The final section deals with blood and tissue types and their relevance to blood transfusion and tissue transplantation.

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