Identification Of White Blood Cells Research Articles

Label-free white blood cells classification using a deep feature fusion neural network

White blood cell (WBC) classification is a valuable diagnostic approach for identifying diseases. However, conventional methods for WBC detection, such as flow cytometers, have limitations in terms of their high cost, large system size, and laborious staining procedures. As a result, deep learning-based label-free WBC image analysis methods are gaining popularity. Nevertheless, most existing deep learning WBC classification techniques fail to effectively utilize the subtle differences in the internal structures of WBCs observed under a microscope. To address this issue, we propose a neural network with feature fusion in this study, which enables the detection of label-free WBCs. Unlike conventional convolutional neural networks (CNNs), our approach combines low-level features extracted by shallow layers with high-level features extracted by deep layers, generating fused features for accurate bright-field WBC identification. Our method achieves an accuracy of 80.3 % on the testing set, demonstrating a potential solution for deep-learning-based biomedical diagnoses. Considering the proposed method simplifies the cell detection process and eliminates the need for complex operations like fluorescent staining, we anticipate that this automatic and label-free WBC classification network could facilitate more precise and effective analysis, and it could contribute to the future adoption of miniatured flow cytometers for point-of-care (POC) diagnostics applications.

Advances in Medical Imaging: Using Convolutional Neural Networks for White Blood Cell Identification

Advances in Medical Imaging: Using Convolutional Neural Networks for White Blood Cell Identification

High-Throughput Sorting and Single-Cell Mechanotyping by Hydrodynamic Sorting-Mechanotyping Cytometry.

The existence of many background blood cells hinders the accurate identification of circulating tumor cells (CTCs) in the blood of cancer patients. To unlock this limitation, a hydrodynamic sorting-mechanotyping cytometry (HSMC) integrated with a sorting-concentration chip and a detection chip is proposed for simultaneously achieving the high-throughput cell sorting and the multi-parameter mechanotyping of the sorted tumor cells. The HSMC adopts the spiral inertial microfluidics for label-free sorting of cells in a high-throughput manner, allowing the efficient enrichment of tumor cells from the large background blood cells. Then, the sorted cells are concentrated by the concentration unit and finally passed through the detection unit for hydrodynamic deformation. The HSMC has a high throughput for sorting and detection and can successfully reveal the differences in the cellular mechanical properties. After characterizing and optimizing the single chips, the identification of white blood cells (WBCs) and three types of tumor cells (A549, MCF-7, and MDA-MB-231 cells) is successfully achieved. The identification accuracies for WBCs and different tumor cells are all larger than 94%, while the highest identification accuracy is up to 99.2%. This study envisions that the HSMC will offer an avenue for the analysis of single cell intrinsic mechanics in clinical medicine.

Integrating explainability into deep learning-based models for white blood cells classification

White blood cells (WBCs) are crucial constituents of the blood that protect the human body against infections and viruses. The classification of WBCs in a blood smear image is used to diagnose a range of haematological disorders. The manual identification of WBCs can result in potential errors, necessitating the need for automated systems that can assist in classification. Recently, various deep learning models, such as DenseNet121, Xception, MobileNetV2, ResNet50, and VGG16, have been used to classify WBCs. However, the available classification models are black boxes because their decisions are difficult for humans to understand without further exploration. The interpretability and explainability of these models are essential, as their decisions can have severe consequences for patients. In this paper, we integrate an explainable AI (XAI) technique called local interpretable model-agnostic explanations (LIME) with the DenseNet121 classification model for WBC classification. Interpretable results would allow the users to understand and verify the model’s predictions, enhancing their confidence in the automated diagnosis.

A SURVEY ON VARIOUS APPROACHES FOR SEGMENTATION AND CLASSIFICATION OF WHITE BLOOD CELLS

Traditionally, White Blood Cell identification is performed by experienced pathologists manually. Peripheral blood smear analysis is a laboratory procedure that helps in diagnosis of various pathological disorders such as malaria, anemia, leukemia, etc. This process is performed at a microscopic level and therefore is required to be precise. Manually performing microscopic evaluation is a challenging task. This task can be automated with the help of computer-aided systems. This work discusses various methods being used in a variety of cellular image segmentation and classification tasks with the field of the survey being concentrated on leukocyte segmentation and classification.

Improving White Blood Cell Morphology Education Through Standardized Questions

Medical laboratory scientists routinely evaluate blood smears in the clinical laboratory. A high level of skill in white blood cell morphology is required to do the task quickly and accurately. Educational approaches to teaching white blood cell morphology consist of showing examples of cells to students and allowing them to practice using trial and error to hone their skills. Student progress with this traditional approach is often quite slow. In order to expedite the learning process, we created a series of questions that can guide morphological identification; these questions focus on the shape and description of the nucleus, cytoplasm, granules and cell overall. We hypothesized that white blood cells can be accurately and uniformly identified using the criteria we’ve outlined. We confirmed the accuracy of this approach to white blood cell identification using a random forest classifier algorithm on a data set of 3600 cells that we had previously categorized. Our approach has the potential to standardize white blood cell morphology training in undergraduate medical laboratory scientist programs, and could help improve the consistency of manual differential results between hospital laboratories. This work is funded by a Teaching Development Grant from the Brigham Young University College of Life Sciences This is the full abstract presented at the American Physiology Summit 2023 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Fully integrated point-of-care blood cell count using multi-frame morphology analysis

Point-of-care testing (POCT) of blood cell count (BCC) is an emerging approach that allows laypersons to identify and count whole blood cells through simple manipulation. To date, POCTs for BCC were mainly achieved by “stationary” images through blood smears or single-laity arranged cells in the microwell, making it difficult to obtain statistically sufficient numbers of cells. In this work, we present a fully integrated POCT device solely using “in-flow” imaging of 3 μL fingertip whole blood for improved identification and counting accuracy of BCC analysis. A miniaturized magnetic stirring module was integrated to maintain the temporal stability of cell concentration. A relatively high throughput (∼8000 cells/min) with a 30-fold dilution ratio of whole blood can be tested for as long as 1 h to examine sufficient numbers of cells, and the subclass cell concentration keeps constant. To improve the identification accuracy, multi-frame “in-flow” imaging was used to track the cell motion trails with multi-angle morphology analysis. This proof-of-concept was then validated with healthy whole blood samples and 75 cases of clinical patients with abnormal concentrations of red blood cells (RBCs), white blood cells (WBCs), and platelets (PLT). The average precision (AP) value of WBCs identification was improved from 0.8622 to 0.9934 using the multi-frame analysis method. And the high fitting degrees (>0.98) between our POCT device and the commercial clinical equipment indicated good agreement. This POCT device is user-friendly and cost-effective, making it a potential tool for diagnosing abnormal blood cell morphology or concentration in the field setting.

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

BackgroundVia 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 classification of WBCs is crucial. Unfortunately, the manual microscopic evaluation is complicated, time-consuming, and subjective, so its statistical reliability becomes limited. Hence, the automatic and accurate identification of WBCs is of great benefit. However, the similarity between WBC samples and the imbalance and insufficiency of samples in the field of medical computer vision bring challenges to intelligent and accurate classification of WBCs. To tackle these challenges, this study proposes a deep learning framework by coupling the pre-trained ResNet and DenseNet with SCAM (spatial and channel attention module) for accurately classifying WBCs.ResultsIn the proposed network, ResNet and DenseNet enables information reusage and new information exploration, respectively, which are both important and compatible for learning good representations. Meanwhile, the SCAM module sequentially infers attention maps from two separate dimensions of space and channel to emphasize important information or suppress unnecessary information, further enhancing the representation power of our model for WBCs to overcome the limitation of sample similarity. Moreover, the data augmentation and transfer learning techniques are used to handle the data of imbalance and insufficiency. In addition, the mixup approach is adopted for modeling the vicinity relation across training samples of different categories to increase the generalizability of the model. By comparing with five representative networks on our developed LDWBC dataset and the publicly available LISC, BCCD, and Raabin WBC datasets, our model achieves the best overall performance. We also implement the occlusion testing by the gradient-weighted class activation mapping (Grad-CAM) algorithm to improve the interpretability of our model.ConclusionThe proposed method has great potential for application in intelligent and accurate classification of WBCs.

Classification and recognition method of white blood cells subclasses in batches based on phase characteristics with non-orthogonal phase imaging

In this study, a novel identification method of white blood cells (WBCs) is proposed. Firstly, five phase characteristic parameters are introduced to characterize the detailed information used for WBCs identification. Secondly, a phase information acquisition device can simultaneously obtain two non-orthogonal phases of WBCs in batches, thereby improving the recognition accuracy. Finally, combining the threshold of the parameters and two phases in non-orthogonal directions, a label-free recognition process of WBCs subclasses based on phase features is established. Experimental results show that the method is feasible and provides new ideas and a technical basis for the analysis and classification of blood cells.

Detection of White Blood Cell Cancer using Deep Learning using Cmyk-Moment Localisation for Information Retrieval

Medical diagnosis, notably concerning tumors, has been transformed by artificial intelligence as well as deep neural network. White blood cell identification, in particular, necessitates effective diagnosis and therapy. White Blood Cell Cancer (WBCC) comes in a variety of forms. Acute Leukemia Lymphocytes (ALL), Acute Myeloma Lymphocytes (AML), Chronic Leukemia Lymphocytes (CLL), and Chronic Myeloma Lymphocytes (CML) are white blood cell cancers for which detection is time-consuming procedure, vulnerable to sentient as well as equipment blunders. Despite just a comprehensive review with a competent examiner, it can be hard to render a precise conclusive determination in some cases. Conversely, Computer-Aided Diagnosis (CAD) may assist in lessening the number of inaccuracies as well as duration spent in diagnosing WBCC. Though deep learning is widely regarded as the most advanced method for detecting WBCCs, the richness of the retrieved attributes employed in developing the pixel-wise categorization algorithms has a substantial relationship with the efficiency of WBCC identification. The investigation of the various phases of alterations related with WBC concentrations and characteristics is crucial to CAD. Leveraging image handling plus deep learning technologies, a novel fusion characteristic retrieval technique has been created in this research. The suggested approach is divided into two parts: 1) The CMYK-moment localization approach is applied to define the Region of Interest (ROI) and 2) A CNN dependent characteristic blend strategy is utilized to obtain deep learning characteristics. The relevance of the retrieved characteristics is assessed via a variety of categorization techniques. The suggested component collection approach versus different attributes retrieval techniques is tested with an exogenous resource. With all the predictors, the suggested methodology exhibits good effectiveness, adaptability, including consistency, exhibiting aggregate categorization accuracies of 97.57 percent and 96.41 percent, correspondingly, utilizing the main as well as auxiliary samples. This approach has provided a novel option for enhancing CLL identification that may result towards a more accurate identification of malignancies.

Discrimination of tumor cell type based on cytometric detection of dielectric properties

In this paper, a microfluidic impedance cytometer (MIC) was employed to analyze the dielectric properties of human white blood cells (WBCs) and four tumor cell lines and realize the label-free identification of cell types. The impedance of cells was detected using an asymmetric serpentine microchannel based MIC under four different frequencies simultaneously. The asymmetric serpentine microchannel achieved the elasto-inertial focusing of cells into a single train, ensuring accurate impedance detection of cells. Various dielectric parameters (cell diameters, impedance amplitude |Z|, impedance phase shift ΦZ, and electric opacities |Z|HF/|Z|LF, ΦZHF/ΦZLF, Re(ZHF)/Re(ZLF), and Im(ZHF)/Im(ZLF)) were defined and used to analyze the dielectric properties of cells. The obtained dielectric parameters were used to train machine learning classification models for identifying cell types. Using all parameters proposed in this paper (cell diameter, opacity |Z|HF/|Z|LF, ΦZHF/ΦZLF, Re(ZHF)/Re(ZLF), and Im(ZHF)/Im(ZLF)) to train the classification model, the true positive rate (TPR) for the identification of WBCs, A549, MCF7, H226, and H460 cells were 99.6%, 96.2%, 99.1%, 97.6%, and 97.2%, respectively. Results showed that our MIC provided a promising method for label-free discrimination of circulating tumor cells in multiple primary cancers.

Cross Validation Analysis of Convolutional Neural Network Variants with Various White Blood Cells Datasets for the Classification Task

White Blood Cells (WBCs) analysis is an important procedure to detect diseases is that closely related to human immunity system. Manual WBCs analysis is laborious and hence computer aided system (CAD) is a better option to alleviate the shortcoming. Since conventional segmentation-classification approach is tedious to configure, a Convolutional Neural Network (CNN) become recent trend for WBCs classification. Previously, there are many works proposed for WBCs identification. However, the models that can be generalised to works well among various datasets is remain vague. In this paper, an analysis of various CNN models which are simple Alexnet, embedded friendly Mobilenet, inception based Googlenet, systematic architecture VGG-16 and skip connection based model (Resnet & Densenet), are tested with three major WBCs datasets (Kaggle, LISC and IDB-2). From the rigorous experiments, it can be concluded that simple CNN model of Alexnet performs well across all three datasets with 98.08% accuracy on Kaggle, 96.34% accuracy on IDB-2 and 84.52% on LISC. This outcome can be utilise as a basis to improve the CNN classification model that can be generalize to works under various WBCs datasets.

Identification of white blood cells for the diagnosis of acute myeloid leukemia

AbstractThe diagnosis of hematologic malignancies such as leukemia includes identifying malignant white blood cells. Manual microscopic analysis of white blood cells is time‐consuming and involves the assistance of medical specialists, and its accuracy may be affected by their abilities. Our objective is to develop an automated system that can identify 15 categories of white blood cells to assist medical practitioners in diagnosing acute myeloid leukemia. The proposed approach uses an ensemble model built with transfer learning‐based pre‐trained networks to categorize 15 kinds of white blood cells. An over‐sampling strategy is used to alleviate the problem of class imbalance, followed by data augmentation. For experimentation, a microscopic blood image dataset containing 18 365 cells images from 200 individuals representing 15 different forms of leukocytes is used. For cells with more than 400 images available, the suggested technique achieves an F1 score of more than 91%. Myeloblasts, which are frequent in myeloid leukemias and are detected in the peripheral blood, are recognized with an average precision of 95.74%. This work describes an image processing strategy that employs ensemble learning to assist in diagnosing acute myeloid leukemia by classifying 15 different types of leukocytes. Experiments show that our technique is both practical and effective. Extensive research supports the use of the leukocyte classifier in real‐world medical applications, such as assisting clinicians in diagnosing diseases and decreasing human resource requirements.

WBCs-Net: type identification of white blood cells using convolutional neural network

WBCs-Net: type identification of white blood cells using convolutional neural network

Hemoglobin quantification in red blood cells via dry mass mapping based on UV absorption

.Significance: The morphological properties and hemoglobin (Hb) content of red blood cells (RBCs) are essential biomarkers to diagnose or monitor various types of hematological disorders. Label-free mass mapping approaches enable accurate Hb quantification from individual cells, serving as promising alternatives to conventional hematology analyzers. Deep ultraviolet (UV) microscopy is one such technique that allows high-resolution, molecular imaging, and absorption-based mass mapping.Aim: To compare UV absorption-based mass mapping at four UV wavelengths and understand variations across wavelengths and any assumptions necessary for accurate Hb quantification.Approach: Whole blood smears are imaged with a multispectral UV microscopy system, and the RBCs’ dry masses are computed. This approach is compared to quantitative phase imaging-based mass mapping using data from an interferometric UV imaging system.Results: Consistent Hb mass and mean corpuscular Hb values are obtained at all wavelengths, with the precision of the single-cell mass measurements being nearly identical at 220, 260, and 280 nm but slightly lower at 300 nm.Conclusions: A full hematological analysis (including white blood cell identification and characterization, and Hb quantification) may be achieved using a single UV illumination wavelength, thereby improving the speed and cost-effectiveness.

White blood cell type identification using multi-layer convolutional features with an extreme-learning machine

White blood cells (WBCs) are the main component of the immune system that have a major role in protecting the body against different types of infections arising due to viruses, bacteria, fungi, and so on. The WBCs are generally referred to as 5 main subtypes: lymphocytes, monocytes, neutrophils, eosinophils, and basophils. Recognizing and counting each type of WBC is important for diagnosing and treating various disorders, such as infectious diseases, autoimmune disorders, immune deficiencies, leukemia, etc. To this end, a fast and accurate WBC classification model is crucial. This study offers a new model that works with a deep neural network—namely, multi-layer (ML) convolutional features of the AlexNet architecture followed by a feature selection (FS) strategy (MLANet-FS) for WBC-type identification. The proposed model exploits multi-layer convolutional features from different layers of the AlexNet model to provide rich discriminative detail, because different convolutional layers contain different visual characteristics of WBCs, and thereafter, linear fusion of these features occurs automatically. FS strategy is used to select the most distinguishing features from the feature fusion pool. Next, an extreme-learning machine (ELM) is employed to learn a discriminative model of WBC type identification. The proposed MLANet-FS-ELM model was evaluated in extensive experiments on the WBC benchmark dataset. It achieved 99.99% training accuracy and 99.12% testing accuracy, demonstrating that the proposed model outperforms alternative methods in the literature developed for WBC identification.

Identification of White Blood Cells Using Machine Learning Classification Based on Feature Extraction

In various disease diagnoses, one of the parameters is white blood cells, consisting of eosinophils, basophils, neutrophils, lymphocytes, and monocytes. Manual identification takes a long time and tends to be subjective depending on the staff's experience, so the automatic identification of white blood cells will be faster and more accurate. White blood cells are identified by examining a colored blood smear (SADT) and examined under a digital microscope to obtain a cell image. Image identification of white blood cells is determined through HSV color space segmentation (Hue, Saturation Value) and feature extraction of the Gray Level Cooccurrence Matrix (GLCM) method using the Angular Second Moment (ASM), Contrast, Entropy, and Inverse Different Moment (IDM) features. The purpose of this study was to identify white blood cells by comparing the classification accuracy of the K-nearest neighbor (KNN), Naïve Bayes Classification (NBC), and Multilayer Perceptron (MLP) methods. The classification results of 100 training data and 50 white blood cell image testing data. Tests on the KNN, NBC, and MLP methods yielded an accuracy of 82%, 80%, and 94%, respectively. Therefore, MLP was chosen as the best classification model in the identification of white blood cells.

WBC-Net: A white blood cell segmentation network based on UNet++ and ResNet

The counting and identification of white blood cells (WBCs, i.e., leukocytes) in blood smear images play a crucial role in the diagnosis of certain diseases, including leukemia, infections, and COVID-19 (corona virus disease 2019). WBC image segmentation lays a firm foundation for automatic WBC counting and identification. However, automated WBC image segmentation is challenging due to factors such as background complexity and variations in appearance caused by histological staining conditions. To improve WBC image segmentation accuracy, we propose a deep learning network called WBC-Net, which is based on UNet++ and ResNet. Specifically, WBC-Net designs a context-aware feature encoder with residual blocks to extract multi-scale features, and introduces mixed skip pathways on dense convolutional blocks to obtain and fuse image features at different scales. Moreover, WBC-Net uses a decoder incorporating convolution and deconvolution to refine the WBC segmentation mask. Furthermore, WBC-Net defines a loss function based on cross-entropy and the Tversky index to train the network. Experiments on four image datasets show that the proposed WBC-Net achieves better WBC segmentation performance than several state-of-the-art methods.

Segmentation of White Blood Cell, Nucleus and Cytoplasm in Digital Haematology Microscope Images: A Review-Challenges, Current and Future Potential Techniques.

Segmentation of white blood cells in digital haematology microscope images represents one of the major tools in the diagnosis and evaluation of blood disorders. Pathological examinations are being the gold standard in many haematology and histophathology, and also play a key role in the diagnosis of diseases. In clinical diagnosis, white blood cells are analysed by pathologists from peripheral blood smears samples of patients. This analysis is mainly based on morphological features and characteristics of the white blood cells and their nuclei and cytoplasm, including, shapes, sizes, colours, textures, maturity stages and staining processes. Recently, Computer Aided Diagnosis techniques have been rapidly growing in the digital haematology area related to white blood cells, and their nuclei and cytoplasm detection, as well as their segmentation and classification techniques. In digital haematology image analysis, these techniques have played and will continue to play, a vital role for providing traceable clinical information, consolidating pertinent second opinions, and minimizing human intervention. This study outlines, discusses, and introduces the major trends from a particular review of detection and segmentation methods for white blood cells and their nuclei and cytoplasm from digital haematology microscope images. Performance of existing methods have been comprehensively compared, taking into account databases used, number of images and limitations. This study can also help us to identify the challenges that remain, in achieving a robust analysis of white blood cell microscope images, which could support the diagnosis of blood disorders and assist researchers and pathologists in the future. The impact of this work is to enhance the accuracy of pathologists' decisions and their efficiency, and overall benefit the patients for faster and more accurate diagnosis. The significant of the paper on intelligent system is that provides future potential techniques for solving overlapping white blood cell identification and other problems microscopic images. The accurate segmentation and detection of white blood cells can increase the accuracy of cell counting system for diagnosing diseases in the future.

Red blood cells and white blood cells detection by image processing

The common method of red and white blood cells identification and counting consider the manual processes on microscope which is arranged by the laboratory’s technician with their own experience. In this research, we will develop a computer program to detect and identify the proposed objects based on their pattern. The proposed objects are Red Blood Cells (RBCs), and White Blood Cells (WBCs). For blood cells identification and classification, an idea of Viola and Jones will be followed. Adaboost (adaptive boosting) method will be applied to increase the accuracy of the error of learning algorithm. The output of the proposed program shows that all the types of cells mentioned can be detected and classify effectively by showing the number and time spent of cells detected.