scholarly journals Classification of Human White Blood Cells Using Machine Learning for Stain‐Free Imaging Flow Cytometry

2019 ◽  
Vol 97 (3) ◽  
pp. 308-319 ◽  
Author(s):  
Maxim Lippeveld ◽  
Carly Knill ◽  
Emma Ladlow ◽  
Andrew Fuller ◽  
Louise J Michaelis ◽  
...  
Keyword(s):  
Machine Learning ◽  
Flow Cytometry ◽  
Blood Cells ◽  
White Blood Cells ◽  
Imaging Flow Cytometry ◽  
Human White Blood Cells

scholarly journals Semi-automated classification of colonial Microcystis by FlowCAM imaging flow cytometry in mesocosm experiment reveals high heterogeneity during seasonal bloom

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yersultan Mirasbekov ◽  
Adina Zhumakhanova ◽  
Almira Zhantuyakova ◽  
Kuanysh Sarkytbayev ◽  
Dmitry V. Malashenkov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Flow Cytometry ◽  
Spatial Resolution ◽  
Mesocosm Experiment ◽  
Imaging Flow Cytometry ◽  
Leibler Divergence ◽  
Temporal And Spatial ◽  
High Level ◽  
Training Sets

AbstractA machine learning approach was employed to detect and quantify Microcystis colonial morphospecies using FlowCAM-based imaging flow cytometry. The system was trained and tested using samples from a long-term mesocosm experiment (LMWE, Central Jutland, Denmark). The statistical validation of the classification approaches was performed using Hellinger distances, Bray–Curtis dissimilarity, and Kullback–Leibler divergence. The semi-automatic classification based on well-balanced training sets from Microcystis seasonal bloom provided a high level of intergeneric accuracy (96–100%) but relatively low intrageneric accuracy (67–78%). Our results provide a proof-of-concept of how machine learning approaches can be applied to analyze the colonial microalgae. This approach allowed to evaluate Microcystis seasonal bloom in individual mesocosms with high level of temporal and spatial resolution. The observation that some Microcystis morphotypes completely disappeared and re-appeared along the mesocosm experiment timeline supports the hypothesis of the main transition pathways of colonial Microcystis morphoforms. We demonstrated that significant changes in the training sets with colonial images required for accurate classification of Microcystis spp. from time points differed by only two weeks due to Microcystis high phenotypic heterogeneity during the bloom. We conclude that automatic methods not only allow a performance level of human taxonomist, and thus be a valuable time-saving tool in the routine-like identification of colonial phytoplankton taxa, but also can be applied to increase temporal and spatial resolution of the study.

Investigation of human white blood cells: application to scanning flow cytometry

2006 ◽  
Author(s):  
Valery A. Loiko ◽  
Gennady I. Ruban ◽  
Olga A. Gritsai ◽  
Alexander A. Miskevich ◽  
Svetlana M. Kosmacheva ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Blood Cells ◽  
White Blood Cells ◽  
Human White Blood Cells

scholarly journals Review of Machine Learning Applications and Datasets in Classification of Acute Leukemia

2021 ◽  
Vol 11 (6) ◽  
pp. 1-9
Author(s):  
Jaishree Ranganathan
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Acute Leukemia ◽  
Blood Cells ◽  
White Blood Cells ◽  
Cell Types ◽  
Research Community ◽  
Machine Learning Applications ◽  
Different Sources

Cancer is an extremely heterogenous disease. Leukemia is a cancer of the white blood cells and some other cell types. Diagnosing leukemia is laborious in a multitude of areas including heamatology. Machine Learning (ML) is the branch of Artificial Intelligence. There is an emerging trend in ML models for data classification. This review aimed to describe the literature of ML in the classification of datasets for acute leukemia. In addition to describing the existing literature, this work aims to identify different sources of publicly available data that could be utilised for research and development of intelligent machine learning applications for classification. To best of the knowledge there is no such work that contributes such information to the research community.

scholarly journals An Assessment of Gpu and Cpu based Convolutional Neural Network for Classification of White Blood Cells

2020 ◽  
Vol 9 (7) ◽  
pp. 554-559
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Blood Cell ◽  
Blood Cells ◽  
White Blood Cells ◽  
Image Data ◽  
The Body ◽  
Machine Learning Techniques ◽  
Vast Number

White blood cell (Leukocytes) is made up of bone marrow located in the blood and lymph tissue. They are portion of the human body’s immune system, thereby helping the body system to fight against infection and other related diseases. The number of leukocytes in the blood is usually part of a complete blood cell (CBC) test, which may be used to check for conditions such as infection, inflammation, allergies, and leukemia. Automation of variance count of leukocytes offers valuable information to medical pathologist to diagnose and treat of many blood based diseases. Early characterization and classification of blood sample is a major lacuna in the medical field, giving rise to lots of challenges for pathologist to adequately predict blood based disease. Several successful efforts have been made to address the aforementioned challenges with the use of machine learning generally and Convolution Neural Network in particular. However the processor configuration which can result in real time, and accurate classification of the high dimensional pattern is imminent, and a vast number of researchers are not explicit on the system configuration used to obtain the result in their report, which is the crux of this research. In this research,12,500 augment images of blood cells was obtained from the Kaggle Repository online. The leukocytes are contained in the blood smear image and categorized into five major types of their types: Neutrophil, Eosinophil, Basophil, Lymphocyte and Monocyte. The color, geometric and texture features are used by the pathologists to differentiate the leukocytes. The Simulation was done using python programing language and python libraries including Keras, pandas, sklearn, numpy, scipy and matplot for potting of graphs of results. The simulation was done on both CPU and GPU processor to compare the performance of the processors on CNNs based classification of the data. While CPU has faster clock speed GPU has more cores. Hence the evaluation metrics used which are precision, specificity, sensitivity, training accuracy and validation accuracy revealed that GPU processor outperforms CPU in terms of the stated metrics of comparison. Therefore a high configuration processor (GPU), which handles graphics better is recommended for processing image data that involves the use of machine learning techniques

scholarly journals Review of Machine Learning Applications and Datasets in Classification of Acute Leukemia

2021 ◽  
Vol 11 (6) ◽  
pp. 1-9
Author(s):  
Jaishree Ranganathan
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Acute Leukemia ◽  
Blood Cells ◽  
White Blood Cells ◽  
Cell Types ◽  
Research Community ◽  
Machine Learning Applications ◽  
Different Sources

Cancer is an extremely heterogenous disease. Leukemia is a cancer of the white blood cells and some other cell types. Diagnosing leukemia is laborious in a multitude of areas including heamatology. Machine Learning (ML) is the branch of Artificial Intelligence. There is an emerging trend in ML models for data classification. This review aimed to describe the literature of ML in the classification of datasets for acute leukemia. In addition to describing the existing literature, this work aims to identify different sources of publicly available data that could be utilised for research and development of intelligent machine learning applications for classification. To best of the knowledge there is no such work that contributes such information to the research community.

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