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Leukocytes

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WBC-based segmentation and classification on microscopic images: a minor improvement.

F1000Research
Introduction White blood cells (WBCs) are immunity cells which fight against viruses and bacteria in the human body. Microscope images of captured WBCs for processing and analysis are important to interpret the body condition. At present, there is no...

Leukocyte Segmentation Method Based on Adaptive Retinex Correction and U-Net.

Computational and mathematical methods in medicine
To address the issues of uneven illumination and inconspicuous leukocyte properties in the gathered cell pictures, a leukocyte segmentation method based on adaptive retinex correction and U-net was proposed. The procedure begins by processing a perip...

Increasing a microscope's effective field of view via overlapped imaging and machine learning.

Optics express
This work demonstrates a multi-lens microscopic imaging system that overlaps multiple independent fields of view on a single sensor for high-efficiency automated specimen analysis. Automatic detection, classification and counting of various morpholog...

WBC-AMNet: Automatic classification of WBC images using deep feature fusion network based on focalized attention mechanism.

PloS one
The recognition and classification of White Blood Cell (WBC) play a remarkable role in blood-related diseases (i.e., leukemia, infections) diagnosis. For the highly similar morphology of different WBC subtypes, it is too confused to classify the WBC ...

Machine Learning Based Lens-Free Shadow Imaging Technique for Field-Portable Cytometry.

Biosensors
The lens-free shadow imaging technique (LSIT) is a well-established technique for the characterization of microparticles and biological cells. Due to its simplicity and cost-effectiveness, various low-cost solutions have been developed, such as autom...

WBC image classification and generative models based on convolutional neural network.

BMC medical imaging
BACKGROUND: Computer-aided methods for analyzing white blood cells (WBC) are popular due to the complexity of the manual alternatives. Recent works have shown highly accurate segmentation and detection of white blood cells from microscopic blood imag...

The Effect of Data Augmentation in Deep Learning Approach for Peripheral Blood Leukocyte Recognition.

Studies in health technology and informatics
Data augmentation is reported as a useful technique to generate a large amount of image datasets from a small image dataset. The aim of this study is to clarify the effect of data augmentation for leukocyte recognition with deep learning. We performe...

Accurate classification of white blood cells by coupling pre-trained ResNet and DenseNet with SCAM mechanism.

BMC bioinformatics
BACKGROUND: Via counting the different kinds of white blood cells (WBCs), a good quantitative description of a person's health status is obtained, thus forming the critical aspects for the early treatment of several diseases. Thereby, correct classif...

Detection of live breast cancer cells in bright-field microscopy images containing white blood cells by image analysis and deep learning.

Journal of biomedical optics
SIGNIFICANCE: Circulating tumor cells (CTCs) are important biomarkers for cancer management. Isolated CTCs from blood are stained to detect and enumerate CTCs. However, the staining process is laborious and moreover makes CTCs unsuitable for drug tes...

Deep Features Aggregation-Based Joint Segmentation of Cytoplasm and Nuclei in White Blood Cells.

IEEE journal of biomedical and health informatics
White blood cells (WBCs), also known as leukocytes, are one of the valuable parts of the blood and immune system. Typically, pathologists use microscope for the manual inspection of blood smears which is a time-consuming, error-prone, and labor-inten...