Classification Of Blood Cells Research Articles

White Blood Cell Classification based on Gray Level Co-occurrence Matrix with Zero Phase Component Analysis Approach

This paper presents an approach to WBC classification by employing Gray Level Co-occurrence Matrix (GLCM) along with the Analysis of Variance (ANOVA) test and Zero Phase Component Analysis (ZCA) whitening. Moreover, the performance is evaluated through the K- Nearest Neighbor (K-NN) classifier. The proposed approach has achieved an accuracy of 94.25% on the Blood Cell Count and Detection (BCCD) dataset for the classification of four categories of WBC namely, lymphocytes, monocytes, neutrophils, and eosinophils. The experimental results reveal an improvement in accuracy (11.05, 8.15, and 14.25%) in comparison to the state-of-the-art approaches i.e., Watershed segmentation, Local Binary Pattern (LBP), and Extreme Learning Machines (ELMs) respectively.

Automatic Classification of White Blood Cells Using Pre-Trained Deep Models

White blood cells (WBCs), which are part of the immune system, help our body fight infections and other diseases. Certain diseases can cause our body to produce fewer WBCs than it needs. For this reason, WBCs are of great importance in the field of medical imaging. Artificial intelligence-based computer systems can assist experts in the analysis of WBCs. In this study, an approach is proposed for the automatic classification of WBCs over five different classes using a pre-trained model. ResNet-50, VGG-19, and MobileNet-V3-Small pre-trained models were trained with ImageNet weights. In the training, validation, and testing processes of the models, a public dataset containing 16,633 images and not having an even class distribution was used. While the ResNet-50 model reached 98.79% accuracy, the VGG-19 model reached 98.19% accuracy, the MobileNet-V3-Small model reached the highest accuracy rate with 98.86%. When the predictions of the MobileNet-V3-Small model are examined, it is seen that it is not affected by class dominance and can classify even the least sampled class images in the dataset correctly. WBCs were classified with high accuracy using the proposed pre-trained deep learning models. Experts can effectively use the proposed approach in the process of analyzing WBCs.

Deep learning approach for segmentation and classification of blood cells using enhanced CNN

The main aim of our proposed work is to segment and classify blood cells using K means algorithm, and also with the help of image processing techniques. A complete blood cell count is very important in the medical analysis for evaluating the total health condition of the body. In the olden days, the blood cells are counted manually with the help of a hem cytometer with other lab equipment and certain chemicals. This technique both time-consuming and challenging. Deep Learning (DL) is an artificial intelligence subset of machine learning that can examine unsupervised information. RBC, or red blood cells, are red cells that are numerous in the blood and are noted for their dark red colour. White blood cells, commonly known as leukocytes, protect the body from infection. Processing tools like MATLAB is used to find the variations in area, perimeter and statistical parameters like mean and standard deviation that separates white blood cells from other blood components. Because of its high accuracy, Enhanced CNN can be used in this study for the categorization and recognition of normal and abnormal blood cell images. Average accuracy of 95% and average precision 0f 0.93 which is higher than existing CNN.

CLASSIFICATION OF CYTOLOGICAL IMAGES OF WHITE BLOOD CELLS USING DEEP LEARNING

Leukocytes, also known as white blood cells, are an important part of the immune system and represent a group of cells that protect the body from infections. The classification of leukocytes is widely used to diagnose various diseases, such as AIDS, leukemia, myeloma, anemia and others. However, traditional methods of classification of leukocytes require a lot of time, and their results are prone to errors. The article implements a computer approach to classification and detection of white blood cells on images of blood cells using deep learning methods based on the application of the method of transferring deep learning models and finetuning them. Deep learning has become one of the most popular areas of artificial intelligence. There are many neural network architectures used in deep learning in solving computer vision problems. The purpose of this study is to develop a computer system for efficient automatic detection and classification of leukocytes of four types of blood cells: eosinophils, lymphocytes, monocytes, neutrophils. This article used popular models of accurate neural networks VGG16, ResNet50, DensNet201, MobileNetV3, InceptionResnetV2, pretrained on ImageNet and finetuned on WBC images dataset.The paper also proposes a custom model of a deep neural network using separable convolutional blocks SeparableConv2D in its architecture. The model is optimized using methods of preprocessing normalization and data augmentation. The is model gave classification metrics of accuracy = 98.84 %, precision = 99.56 %, recall = 98.89 % and f1-score = 99.22 %. The developed model allows, in most cases, to determine with high speed whether a leukocyte belongs to one of the four classes in the image, which indicates the possibility of using the system as an auxiliary tool for hematological blood analysis.

Classification of White Blood Cells: A Comprehensive Study Using Transfer Learning Based on Convolutional Neural Networks.

White blood cells (WBCs) in the human immune system defend against infection and protect the body from external hazardous objects. They are comprised of neutrophils, eosinophils, basophils, monocytes, and lymphocytes, whereby each accounts for a distinct percentage and performs specific functions. Traditionally, the clinical laboratory procedure for quantifying the specific types of white blood cells is an integral part of a complete blood count (CBC) test, which aids in monitoring the health of people. With the advancements in deep learning, blood film images can be classified in less time and with high accuracy using various algorithms. This paper exploits a number of state-of-the-art deep learning models and their variations based on CNN architecture. A comparative study on model performance based on accuracy, F1-score, recall, precision, number of parameters, and time was conducted, and DenseNet161 was found to demonstrate a superior performance among its counterparts. In addition, advanced optimization techniques such as normalization, mixed-up augmentation, and label smoothing were also employed on DenseNet to further refine its performance.

White blood cell detection, classification and analysis using phase imaging with computational specificity (PICS)

Treatment of blood smears with Wright’s stain is one of the most helpful tools in detecting white blood cell abnormalities. However, to diagnose leukocyte disorders, a clinical pathologist must perform a tedious, manual process of locating and identifying individual cells. Furthermore, the staining procedure requires considerable preparation time and clinical infrastructure, which is incompatible with point-of-care diagnosis. Thus, rapid and automated evaluations of unlabeled blood smears are highly desirable. In this study, we used color spatial light interference microcopy (cSLIM), a highly sensitive quantitative phase imaging (QPI) technique, coupled with deep learning tools, to localize, classify and segment white blood cells (WBCs) in blood smears. The concept of combining QPI label-free data with AI for the purpose of extracting cellular specificity has recently been introduced in the context of fluorescence imaging as phase imaging with computational specificity (PICS). We employed AI models to first translate SLIM images into brightfield micrographs, then ran parallel tasks of locating and labelling cells using EfficientNet, which is an object detection model. Next, WBC binary masks were created using U-net, a convolutional neural network that performs precise segmentation. After training on digitally stained brightfield images of blood smears with WBCs, we achieved a mean average precision of 75% for localizing and classifying neutrophils, eosinophils, lymphocytes, and monocytes, and an average pixel-wise majority-voting F1 score of 80% for determining the cell class from semantic segmentation maps. Therefore, PICS renders and analyzes synthetically stained blood smears rapidly, at a reduced cost of sample preparation, providing quantitative clinical information.

Magnetic Fractionation of Red Blood Cells Based on Oxygen Saturation in Sickle Cell Disease

Anemias are a broad classification of red blood cell (RBC) disorders which effectively result in inadequate oxygen transport in the body. Sickle Cell Disease (SCD) results from a point mutation in hemoglobin, and the hemoglobin-oxygen equilibrium becomes right shifted (i.e., SCD RBCs do not oxygen saturate to the same degree as healthy RBCs). RBC exchange transfusion is a common clinical practice to increase the oxygen carrying capacity of a SCD patient - a process by which the patient's own RBCs are exchanged with healthy donor blood. During this process, large amounts of donor RBCs are discarded along with the patients SCD RBCs. Here we present a new RBC separation modality based on hemoglobin-oxygen equilibrium.It is well known that RBCs become magnetized as a function of the oxygen saturation of its hemoglobin. Due to the difference in hemoglobin-oxygen equilibrium for healthy and SCD RBCs we demonstrate a magnetic method of fractionating RBCs based on oxygen saturation. In our study, we use the SCD transfusion waste product and sort this sample into healthy fractions and SCD fractions using a field flow permeant magnetic separator. Under variable oxygen tension of the sample, we preformed the magnetic sorting.By incorporation of magnetic separation into RBC exchange processes, more efficient selection, and removal of SCD RBCs can be accomplished. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Classification of microscopic cervical blood cells using inception ResNet V2 with modified activation function

Cervical cancer is the most frequent and fatal malignancy among women worldwide. If this tumor is detected and treated early enough, the complications it causes can be minimized. Deep learning demonstrated significant promise when imposed on biomedical difficulties such as medical image processing and disease prognostication. Therefore, in this paper, an automatic cervical cell classification approach named IR-PapNet is developed based on Inception-ResNet which is an optimized version of Inception. The learning model’s conventional ReLu activation is replaced with the parametric-rectified linear unit (PReLu) to overcome the nullification of negative values and dying ReLu. Finally, the model loss function is minimized with the SGD optimization model by modifying the attributes of the neural network. Furthermore, we present a simple but efficient noise removal technique called 2D-Discrete Wavelet Transform (2D-DWT) algorithm for enhancing image quality. Experimental results show that this model can achieve a top-1 average identification accuracy of 99.8% on the pap smear cervical Herlev datasets, which verifies its satisfactory performance. The restructured Inception-ResNet network model can obtain significant improvements over most of the state-of-the-art models in 2-class classification, and it achieves a high learning rate without experiencing dead nodes.

White Blood Cell Classification Using Texture and RGB Features of Oversampled Microscopic Images.

White blood cell (WBC) type classification is a task of significant importance for diagnosis using microscopic images of WBC, which develop immunity to fight against infections and foreign substances. WBCs consist of different types, and abnormalities in a type of WBC may potentially represent a disease such as leukemia. Existing studies are limited by low accuracy and overrated performance, often caused by model overfit due to an imbalanced dataset. Additionally, many studies consider a lower number of WBC types, and the accuracy is exaggerated. This study presents a hybrid feature set of selective features and synthetic minority oversampling technique-based resampling to mitigate the influence of the above-mentioned problems. Furthermore, machine learning models are adopted for being less computationally complex, requiring less data for training, and providing robust results. Experiments are performed using both machine- and deep learning models for performance comparison using the original dataset, augmented dataset, and oversampled dataset to analyze the performances of the models. The results suggest that a hybrid feature set of both texture and RGB features from microscopic images, selected using Chi2, produces a high accuracy of 0.97 with random forest. Performance appraisal using k-fold cross-validation and comparison with existing state-of-the-art studies shows that the proposed approach outperforms existing studies regarding the obtained accuracy and computational complexity.

Detection and classification of white blood cells with an improved deep learning-based approach

The analysis of white blood cells, which defend the body against deadly infections and disease-causing substances, is an important issue in the medical world. The concentrations of these cells in the blood, examined in 5 classes, i.e. monocytes, eosinophils, basophils, lymphocytes, and neutrophils, vary according to the types of diseases in the body. The peripheral blood smear is widely used to analyze blood cells. Manual evaluation of this method is laborious and time-consuming. At the same time, many environmental and humanistic parameters affect the method's performance. Therefore, in the presented study, a real-time detection process is realized. Firstly, YOLOv5s, YOLOv5x, and Detectron 2 R50-FPN pretrained models in the object recognition framework are used. Next, two original contributions are made to the study to improve the model's performance. The first contribution includes optimizing the activation function, an essential criterion in training the model, and an arrangement provided in the architecture. With this proposed approach, an improvement of 0.006 is achieved in the recognition rates of all classes. The second contribution is the combined use of the YOLO and Detectron2 frameworks, which have two different object evaluation processes. The success rate achieved with this hybrid structure provided an improvement between 3.44% and 14.7% compared to the outputs obtained from the YOLO and Detectron2 pretrained models. In addition, the maximum accuracy rate of this hybrid structure on the test dataset for detection and classification of white blood cells is obtained as 98%.

Data augmentation with improved regularisation and sampling for imbalanced blood cell image classification

Due to progression in cell-cycle or duration of storage, classification of morphological changes in human blood cells is important for correct and effective clinical decisions. Automated classification systems help avoid subjective outcomes and are more efficient. Deep learning and more specifically Convolutional Neural Networks have achieved state-of-the-art performance on various biomedical image classification problems. However, real-world data often suffers from the data imbalance problem, owing to which the trained classifier is biased towards the majority classes and does not perform well on the minority classes. This study presents an imbalanced blood cells classification method that utilises Wasserstein divergence GAN, mixup and novel nonlinear mixup for data augmentation to achieve oversampling of the minority classes. We also present a minority class focussed sampling strategy, which allows effective representation of minority class samples produced by all three data augmentation techniques and contributes to the classification performance. The method was evaluated on two publicly available datasets of immortalised human T-lymphocyte cells and Red Blood Cells. Classification performance evaluated using F1-score shows that our proposed approach outperforms existing methods on the same datasets.

White Blood Cell Classification Using Multi-Attention Data Augmentation and Regularization

Accurate and robust human immune system assessment through white blood cell evaluation require computer-aided tools with pathologist-level accuracy. This work presents a multi-attention leukocytes subtype classification method by leveraging fine-grained and spatial locality attributes of white blood cell. The proposed framework comprises three main components: texture-aware/attention map generation blocks, attention regularization, and attention-based data augmentation. The developed framework is applicable to general CNN-based architectures and enhances decision making by paying specific attention to the discriminative regions of a white blood cell. The performance of the proposed method/model was evaluated through an extensive set of experiments and validation. The obtained results demonstrate the superior performance of the model achieving 99.69 % accuracy compared to other state-of-the-art approaches. The proposed model is a good alternative and complementary to existing computer diagnosis tools to assist pathologists in evaluating white blood cells from blood smear images.

A COMPARATIVE EVALUATION OF DEEP LEARNING METHODS IN DIGITAL IMAGE CLASSIFICATION

White Blood Cells are important in determining a person's overall health. The blood disease diagnosis includes characterization and identification of blood samples of a patient. Neural Networks (NN), Convolutional Neural Networks (CNN), and a mix of CNN and NN models are used in recent techniques to improve visual content understanding. From start to finish, The authors were driven to uncover remarkable characteristics in example photographs because of their expertise in medical image analysis. For blood cell classification, the overall performance of individual cell patches extracted using blood smear techniques has been excellent. These approaches, on the other hand, are incapable of dealing with the issue of multiple cells overlapping. Because of the blood cell overlapping pictures, the input image dimension is compressed, the classification time is reduced, as well as the network works better with more accurate parameter estimates. In this review, we are evaluating a detailed scientific comparison of some of the ways used to improve WBC classification. The authors will show some of the ways used to automatically classify their cells. The results of some of the tests used using available data, compared to blood cell classification techniques.

A COMPARATIVE EVALUATION OF DEEP LEARNING METHODS IN DIGITAL IMAGE CLASSIFICATION

White Blood Cells are important in determining a person's overall health. The blood disease diagnosis includes characterization and identification of blood samples of a patient. Neural Networks (NN), Convolutional Neural Networks (CNN), and a mix of CNN and NN models are used in recent techniques to improve visual content understanding. From start to finish, The authors were driven to uncover remarkable characteristics in example photographs because of their expertise in medical image analysis. For blood cell classification, the overall performance of individual cell patches extracted using blood smear techniques has been excellent. These approaches, on the other hand, are incapable of dealing with the issue of multiple cells overlapping. Because of the blood cell overlapping pictures, the input image dimension is compressed, the classification time is reduced, as well as the network works better with more accurate parameter estimates. In this review, we are evaluating a detailed scientific comparison of some of the ways used to improve WBC classification. The authors will show some of the ways used to automatically classify their cells. The results of some of the tests used using available data, compared to blood cell classification techniques.

Efficacy and safety of abatacept for interstitial lung disease associated with antisynthetase syndrome: a case series.

This study investigated the efficacy and safety of abatacept (ABA) in interstitial lung disease (ILD) associated with antisynthetase syndrome (ASS). Eight patients were identified through retrospective analysis of the medical records of our centre. All patients fulfilled the Solomon criteria and had a disease complicated with ILD. Lung function, imaging, serum markers, clinical evaluation indicators of ILD, peripheral blood cell classification, cytokines, and prednisone doses were analysed. Seven of the eight patients were female. The mean age was 54.4 (standard deviation [SD] 6.0) years. Antibodies against Jo-1, PL-12, and PL-7 were present in three, three, and two patients respectively. At baseline, the mean diffusing lung capacity for carbon monoxide (DLCO) was 53.8% (SD 9.2%), the mean score of King's Brief Interstitial Lung Disease (KBILD) was 40.6 (SD 13.8), the median Krebs Von den Lungen-6 (KL-6) was 1612.5 (interquartile range [IQR] 1180.5-2431.5) U/ml. All patients experienced symptom alleviation after ABA therapy. The mean and median changes in DLCO percentage, KBILD, and KL-6 were 12.3% (p<0.05), 21.4 (p<0.01), and 174.5U/ml (p<0.01), respectively. No obvious adverse events related to ABA were observed during the treatment. Our study offers preliminary, but encouraging, clinical evidence in favour of ABA as a therapy for ASS-ILD. ABA demonstrated favourable effects on ILD and was well-tolerated. Well-designed randomised controlled studies are required to confirm the efficacy and safety of this strategy.

Deep features based convolutional neural network to detect and automatic classification of white blood cells

Deep features based convolutional neural network to detect and automatic classification of white blood cells

Semi-supervised medical image classification with adaptive threshold pseudo-labeling and unreliable sample contrastive loss

Semi-supervised learning (SSL) may employ unlabeled data to improve model performance, which has great significance in medical imaging tasks. However, pseudo-labeling-based semi-supervised approaches suffer from two problems in medical image datasets: (1) the models' predictions are biased toward the majority class in imbalanced datasets, and (2) discarding unlabeled data with confidence below the thresholds results in the loss of useful information. To solve these issues, we propose a novel SSL framework, FullMatch, which improves the model's performance by utilizing all unlabeled data. Specifically, we propose adaptive threshold pseudo-labeling (ATPL), a method for generating pseudo-labels based on the model's current learning status. ATPL dynamically adjusts the thresholds for each class during the training process, which can generate more pseudo-labels for classes with learning difficulties, thus alleviating the problem of data imbalance. Unlike existing semi-supervised methods based on pseudo-labeling, we do not discard unlabeled data with confidence below the thresholds. We propose an unreliable sample contrastive loss (USCL) to leverage useful information from unlabeled data with confidence below the thresholds by learning the similarities and differences between sample features. To evaluate the performance of the proposed method, we conducted experiments on the ISIC 2018 skin lesion classification dataset and the blood cell classification dataset. The experimental results show that our method outperforms the state-of-the-art SSL methods.

COVID-19 screening with digital holographic microscopy using intra-patient probability functions of spatio-temporal bio-optical attributes.

We present an automated method for COVID-19 screening using the intra-patient population distributions of bio-optical attributes extracted from digital holographic microscopy reconstructed red blood cells. Whereas previous approaches have aimed to identify infection by classifying individual cells, here, we propose an approach to incorporate the attribute distribution information from the population of a given human subjects' cells into our classification scheme and directly classify subjects at the patient level. To capture the intra-patient distribution information in a generalized way, we propose an approach based on the Bag-of-Features (BoF) methodology to transform histograms of bio-optical attribute distributions into feature vectors for classification via a linear support vector machine. We compare our approach with simpler classifiers directly using summary statistics such as mean, standard deviation, skewness, and kurtosis of the distributions. We also compare to a k-nearest neighbor classifier using the Kolmogorov-Smirnov distance as a distance metric between the attribute distributions of each subject. We lastly compare our approach to previously published methods for classification of individual red blood cells. In each case, the methodology proposed in this paper provides the highest patient classification performance, correctly classifying 22 out of 24 individuals and achieving 91.67% classification accuracy with 90.00% sensitivity and 92.86% specificity. The incorporation of distribution information for classification additionally led to the identification of a singular temporal-based bio-optical attribute capable of highly accurate patient classification. To the best of our knowledge, this is the first report of a machine learning approach using the intra-patient probability distribution information of bio-optical attributes obtained from digital holographic microscopy for disease screening.

White blood cell classification via a discriminative region detection assisted feature aggregation network.

White blood cell (WBC) classification plays an important role in human pathological diagnosis since WBCs will show different appearance when they fight with various disease pathogens. Although many previous white blood cell classification have been proposed and earned great success, their classification accuracy is still significantly affected by some practical issues such as uneven staining, boundary blur and nuclear intra-class variability. In this paper, we propose a deep neural network for WBC classification via discriminative region detection assisted feature aggregation (DRFA-Net), which can accurately locate the WBC area to boost final classification performance. Specifically, DRFA-Net uses an adaptive feature enhancement module to refine multi-level deep features in a bilateral manner for efficiently capturing both high-level semantic information and low-level details of WBC images. Considering the fact that background areas could inevitably produce interference, we design a network branch to detect the WBC area with the supervision of segmented ground truth. The bilaterally refined features obtained from two directions are finally aggregated for final classification, and the detected WBC area is utilized to highlight the features of discriminative regions by an attention mechanism. Extensive experiments on several public datasets are conducted to validate that our proposed DRFA-Net can obtain higher accuracies when compared with other state-of-the-art WBC classification methods.

Event-based White Blood Cell Classification from Nailfold Capillaries

Detecting absorption gaps in a nailfold capillary can be used to quantify an estimation of white blood cells (WBCs). Previously, the absorption gaps in a nailfold capillary were usually measured using a standard camera on a fingernail. However, difficulties arise due to low visibility of the gap, the small size of the capillaries, the high speed of WBC movement, and the lack of contrast between the capillary and its environment/background. To address these issues, an event-based WBC image is utilized as input data to detect WBC existence in the nailfold. Specifically, we utilize a dynamic vision sensor (DVS) camera, which can detect a change in luminance on a pixel basis and can produce a stream of asynchronous event output at a microsecond temporal resolution. With the event-based WBC dataset, we conduct a classification task using three different machine learning algorithms: k-nearest neighbors, the decision tree, and random forest. The best result is from random forest with 75.51% accuracy. Based on our evaluation, event-based WBC classification is a promising new approach to detecting WBC presence in nailfold capillaries.