White Blood Cell Imaging Research Articles

Comparing Human-Level and Machine Learning ModelPerformance in White Blood Cell MorphologyAssessment.

There is an increasing research focus on the role of machine learning in the haematology laboratory, particularly in blood cell morphologic assessment. Human-level performance is an important baseline and goal for machine learning. This study aims to assess the interobserver variability and human-level performance in blood cell morphologic assessment. A dataset of 1000 single white blood cell images were independently labelled by 10 doctors and morphology scientists. Interobserver variability was calculated using Fleiss' kappa. Observers' labels were then separated into consensus labels used to determine ground truth, and performance labels used to assess observer performance. A machine learning model was trained and assessed using the same cell images. Explainability images (XRAI and IG) were generated for each of the test images. The Fleiss kappa for all 10 observers was 0.608, indicating substantial agreement between observers. The accuracy of human observers was 95%, with sensitivity 72% and specificity 97%. The accuracy of the machine learning model was 95%, with sensitivity 71% and specificity 97%. The model shared similar performance across labels when compared to humans. Explainability metrics demonstrated that the machine learning model was able to differentiate between the cytoplasm and nucleus of the cells, and used these features to perform predictions. The substantial, though not perfect, agreement between human observers highlights the inherent subjectivity in white blood cell morphologic assessment. A machine learning model performed similarly to human observers in single white blood cell identification. Further research is needed to compare human-level and machine learning performance in ways that more closely reflect the typical process of morphologic assessment.

A deep learning approach for white blood cells image generation and classification using SRGAN and VGG19

The classification of White Blood Cells (WBCs) is crucial for diagnosing diseases, monitoring treatment effectiveness, and understanding how the immune system functions. In this paper, we propose a deep learning approach to classify WBCs using Super Resolution Generative Adversarial Network (SRGAN) and Visual Geometry Group 19 (VGG19). Firstly, microscopic images of WBCs are generated using the SRGAN to obtain more precise and high-resolution images, which are then classified with a pretrained VGG19 classifier. Low-resolution (LR) images are inputted into the generator of SRGAN, and its discriminator compares the High-resolution (HR) image with LR, generating super-resolution images to minimize misclassification risks. A large dataset of 12,447 images containing four classes of WBCs (Eosinophil, Lymphocyte, Monocyte, and Neutrophil) is utilized to train and validate our proposed model. Following extensive experimental analysis, our proposed model achieves a test accuracy of 94.87 %, surpassing traditional Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Hybrid CNN-RNN models, and other conventional approaches. The generated images of SRGAN overcome challenges associated with misclassification due to the poor resolution of microscopic images, while the use of a pretrained model as a classifier reduces classification complexity. The source code of the entire work is available at https://github.com/Jannatul-Ferdousi/SRGAN_VGG19_WBC.git.

Leukocyte classification using relative-relationship-guided contrastive learning

Hematologic diseases and blood disorders can be studied through microscopic examination of blood smear images or chemical assays. Many researchers are focused on utilizing deep learning (DL) to identify, quantify, and classify various types of blood cells, which is crucial for addressing theoretical and practical challenges in disease diagnosis and treatment planning. However, there is a significant deficiency in annotations for leukocyte classification, limiting the effectiveness of current DL methods. Contrastive learning (CL) facilitates the extraction of prior knowledge from unlabeled data, reducing the dependency on manual annotations. While CL demonstrates remarkable achievements in classifying natural images, the direct application to leukocyte classification presents certain defects. We observe that standard data augmentation techniques in CL exhibit varying sensitivity to different categories of white blood cell images. Employing a uniform augmentation strategy may lead the network into an optimization trap, acquiring inadequate representations from erroneous sample relationships. To address this issue, we propose a Relative-Relationship-Guided Contrastive Learning Representation (ReCLR) framework for the leukocyte classification. ReCLR mines positive and negative samples based on relative distance knowledge. Specifically, we provide adversarial guidance for the positive sample, restricting the distance between the positive sample and the original sample less than but as close as possible to the distance between the original sample and the farthest negative sample. Then, we utilize the entropy constraint to regulate the relative distance relationship between the negative and original samples. Finally, the guided positive and negative samples are employed for contrastive learning in leukocyte classification. The extensive experiments, including linear evaluation, domain transfer, and fine-tuning, demonstrate the effectiveness of the proposed method. Our ReCLR achieves accuracies of 92.07%, 65.36%, and 92.49% on three real-world leukocyte datasets, respectively, outperforming several state-of-the-art methods. The source code is released at https://github.com/AlchemyEmperor/ReCLR.

Gold-antibody-aptamer complexed electrochemical sensing surface for septic arthritis biomarker determination

Septic arthritis (SA) is more severe in patients with rheumatoid arthritis, joint surgical issues, or a weakened immune system. Timely diagnosis of SA is crucial for effective treatment. Traditional diagnostic methods such as ELISA, white blood cell counting, blood culture, qPCR, and imaging techniques are often less accurate and time-consuming. Researchers are focusing on developing highly sensitive biosensors for SA using blood-based biomarkers. Procalcitonin is a protein and a well-established biomarker for SA. This research focuses on developing a procalcitonin interdigitated electrode (IDE) biosensor using a probe made of an aptamer and antibody-modified gold nanoparticle (AuNP) complex. The probe was attached to the IDE through an amine linker and then interacted with procalcitonin. AuNPs increased the attachment of the aptamer and antibody to the IDE, enabling the detection of procalcitonin at levels as low as 10 ng/mL, with a linear regression curve ranging from 10 to 100 ng/mL [y = 4.0691x - 2.1887; R2 = 0.9937]. Furthermore, procalcitonin-spiked serum elevated the current level with increasing procalcitonin concentrations, while control performances did not enhance the current, indicating the selective and specific detection of procalcitonin. This AuNP-aptamer-antibody complexed biosensor effectively identifies procalcitonin at low levels and aids in the diagnosis of SA.

Advanced Convolutional Neural Networks for Precise White Blood Cell Subtype Classification in Medical Diagnostics

White blood cell (WBC) classification is pivotal in medical image analysis, playing a critical role in the precise diagnosis and monitoring of diseases. This paper presents a novel convolutional neural network (CNN) architecture designed specifically for the classification of WBC images. Our model, trained on an extensive dataset, automates the extraction of discriminative features essential for accurate subtype identification. We conducted comprehensive experiments on a publicly available image dataset to validate the efficacy of our methodology. Comparative analysis with state-of-the-art methods shows that our approach significantly outperforms existing models in accurately categorizing WBCs into their respective subtypes. An in-depth analysis of the features learned by the CNN reveals key insights into the morphological traits—such as shape, size, and texture—that contribute to its classification accuracy. Importantly, the model demonstrates robust generalization capabilities, suggesting its high potential for real-world clinical implementation. Our findings indicate that the proposed CNN architecture can substantially enhance the precision and efficiency of WBC subtype identification, offering significant improvements in medical diagnostics and patient care.

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.

Diffusion-based Wasserstein generative adversarial network for blood cell image augmentation

White blood cells (WBC) are vital elements of the immune system, and their number and differential count are crucial for diagnosing blood-related disorders. While existing research has primarily focused on classifying easily distinguishable major WBC types, our study delves into a model encompassing up to 19 WBC classes, some of which exhibit irregular shapes and are challenging to differentiate manually. Convolutional Neural Networks (CNNs) have shown remarkable progress in accurately classifying these intricate WBC classes. However, the accuracy of these models depends mainly on the availability of enough appropriate datasets, which can be challenging to obtain for rare WBC classes. To address this, we introduce a generative model, the diffusion-based Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP). This model innovatively combines the Denoising Diffusion Probabilistic Model (DDPM) forward diffusion process with the WGAN-GP, leveraging DDPM’s noisy vectors as inputs for WGAN-GP’s generator. This fusion accelerates the generative process and significantly enhances the output’s fidelity, particularly for complex WBC images. Our model demonstrated its effectiveness on a dataset comprising 4,503 images across 19 WBC classes from Soonchunhyang University Bucheon Hospital, Korea, showing significant improvement in generating high-quality images for rare WBC classes and addressing data imbalance. We further combined pre-trained CNNs with Support Vector Machines (SVM) for classification, where our augmentation strategy led to the ResNet50-SVM model achieving an average accuracy of 95% in classifying the 19 WBC classes. This study not only addresses the data imbalance but also sets a new benchmark in WBC image analysis, demonstrating our model’s efficacy in generating high-quality data for rare classes.

Analysis of Risk Factors for Urosepsis in Patients with Type 2 Diabetes Complicated by Upper Urinary Tract Stones: A Single-Centre Retrospective Study.

Urosepsis represents a complication of upper urinary tract stones (UUTSs) in patients with type 2 diabetes mellitus (T2DM), thus necessitating a comprehensive understanding of risk factors. This single-centre retrospective study aimed to analyse the risk factors for urosepsis in this patient population. Clinical data of patients with UUTS and T2DM admitted from January 2015 to January 2024 were collected and retrospectively analysed. Laboratory parameters, including white blood cell (WBC) count, serum creatinine, urine culture, C-reactive protein and imaging findings were assessed. Stepwise backward selection and logistic regression analysis was used to explore the risk factors of urosepsis. A total of 108 patients, including 56 patients complicated with urosepsis and 52 without urosepsis, were included. The urosepsis group exhibited significantly increased white blood cell count (15.75 ± 2.58 vs. 14.63 ± 2.76, p = 0.031), colony-forming units per millilitre in urine culture (5000.46 ± 1200.56 vs. 4570.13 ± 1000.24, p = 0.045), serum C-reactive protein levels (43.02 ± 12.36 vs. 38.54 ± 10.75, p = 0.047), presence of hydronephrosis (82.14% vs. 63.46%, p = 0.049) , ureteral stricture (46.43% vs. 25.00%, p = 0.034), prevalence of Gram-negative bacteria (85.71% vs. 67.31%, p = 0.042), antibiotic resistance (37.50% vs. 17.31%, p = 0.034), and empirical antibiotic use (62.50% vs. 40.38%, p = 0.035) compared with the non-urosepsis group. Gram-negative bacteria (odds ratio (OR) = 2.914, p = 0.027), antibiotic resistance (OR = 2.867, p = 0.022), renal hydronephrosis (OR = 2.648, p = 0.031), urethral stricture (OR = 2.600, p = 0.022) and antibiotic usage history (OR = 2.460, p = 0.023) exhibited significant OR values, whereas white blood cell (WBC) count demonstrated a moderate OR value (OR = 1.175, p = 0.034). These findings further underscore their potential to be reasonably predictive risk factors for urosepsis. This study identified various risk factors associated with urosepsis in patients with T2DM and UUTS. Laboratory parameters, imaging findings and urinary tract infection characteristics were found to be significant contributors to the development of urosepsis in this patient population.

Automatic recognition of white blood cell images with memory efficient superpixel metric GNN: SMGNN.

An automatic recognizing system of white blood cells can assist hematologists in the diagnosis of many diseases, where accuracy and efficiency are paramount for computer-based systems. In this paper, we presented a new image processing system to recognize the five types of white blood cells in peripheral blood with marked improvement in efficiency when juxtaposed against mainstream methods. The prevailing deep learning segmentation solutions often utilize millions of parameters to extract high-level image features and neglect the incorporation of prior domain knowledge, which consequently consumes substantial computational resources and increases the risk of overfitting, especially when limited medical image samples are available for training. To address these challenges, we proposed a novel memory-efficient strategy that exploits graph structures derived from the images. Specifically, we introduced a lightweight superpixel-based graph neural network (GNN) and broke new ground by introducing superpixel metric learning to segment nucleus and cytoplasm. Remarkably, our proposed segmentation model superpixel metric graph neural network (SMGNN) achieved state of the art segmentation performance while utilizing at most 10000$ \times $ less than the parameters compared to existing approaches. The subsequent segmentation-based cell type classification processes showed satisfactory results that such automatic recognizing algorithms are accurate and efficient to execeute in hematological laboratories. Our code is publicly available at https://github.com/jyh6681/SPXL-GNN.

Retracted: A Method for Expanding the Training Set of White Blood Cell Images

Retracted: A Method for Expanding the Training Set of White Blood Cell Images

White blood cell image analysis for infection detection based on virtual hexagonal trellis (VHT) by using deep learning

White blood cells (WBCs) are an indispensable constituent of the immune system. Efficient and accurate categorization of WBC is a critical task for disease diagnosis by medical experts. This categorization helps in the correct identification of medical problems. In this research work, WBC classes are categorized with the help of a transform learning model in combination with our proposed virtual hexagonal trellis (VHT) structure feature extraction method. The VHT feature extractor is a kernel-based filter model designed over a square lattice. In the first step, Graft Net CNN model is used to extract features of augmented data set images. Later, the VHT base feature extractor extracts useful features. The CNN-extracted features are passed to ant colony optimization (ACO) module for optimal features acquisition. Extracted features from the VHT base filter and ACO are serially merged to create a single feature vector. The merged features are passed to the support vector machine (SVM) variants for optimal classification. Our strategy yields 99.9% accuracy, which outperforms other existing methods.

Performance of the new MC-80 automated digital cell morphology analyser in detection of normal and abnormal blood cells: Comparison with the CellaVision DM9600.

Mindray MC-80 is an automated system for digital imaging of white blood cells (WBCs) and their pre-classification. The objective of this work is to analyse its performance comparing it with the CellaVision® DM9600. A total of 445 samples were used, 194 normal and 251 abnormal: acute leukaemia (100), myelodysplastic syndromes/myeloproliferative neoplasms (33), lymphoid neoplasms (50), plasma cell neoplasms (14), infections (49) and thrombocytopenia (5). WBC pre-classification values with the MC-80 and DM9600 were compared with (1) the microscope, (2) Mindray BC-6800Plus differentials in only normal samples, and (3) confirmed or reclassified images (post-classification). Pearson's correlation, Lin's concordance, Passing-Bablok regression, and Bland-Altman plots were used. Sensitivity, specificity, positive (PPV) and negative (NPV) predictive values for abnormal cells using the MC-80 were calculated. The PPV and NPV were above 98% and 99%, for normal samples. For immature granulocytes (IG), NPV and PPV were 100% and 74.2%. When comparing the WBC differentials using the MC-80, the microscope and the BC-6800Plus, no differences were found except for basophils and IG. Our results showed good agreement between the pre- and post-classification of normal WBC, including IG, quantified by high correlation and concordance values (0.91-1). Sensitivity and specificity for blasts were 0.984 and 0.640. The MC-80 detected abnormal lymphocytes in 30% of the smears from patients with lymphoid neoplasm. Plasma cell identification was better using the DM9600. The sensitivity and specificity for erythroblast detection were 1 and 0.890. We found that the MC-80 shows high performance for WBC differentials for both normal samples and patients with haematological diseases.

Image preprocessing and hyperparameter optimization on pretrained model MobileNetV2 in white blood cell image classification

White blood cells play a role in maintaining the immune system which consists of several types such as neutrophils, lymphocytes, monocytes, eosinophils and basophils. MobileNetV2 is one of the pretrained convolutional neural network (CNN) models that provides excellent advantages and performance in classifying images. In this research was conducted to find out how to apply optimization hyperparameters and the impact of image processing on white blood cell image classification using MobileNetV2, so that it is expected to find a combination of preprocessing and combination of hyperparameter values that can produce the highest accuracy value. To maximize the classification process, before classifying the image, several stages of image preprocessing are carried out, namely cropping, grayscale, resizing and augmentation. Hyperparameter tuning was carried out for an experiment to improve model performance. The three main parameters used in hyperparameter tuning are learning rate, batch size, and number of epochs. Performance optimization model performance will be measured using accuracy, sensitivity, specificity and using a confusion matrix. Based on the experimental results in this study, it shows that the best learning rate value is 0.00001, the best batch size value is 32, and the best epoch value is 250.

Classification of Acute Lymphoblastic Leukemia based on White Blood Cell Images using InceptionV3 Model

Acute lymphoblastic leukemia (ALL) is the most common form of leukemia that occurs in children. Detection of ALL through white blood cell image analysis can help with the prognosis and appropriate treatment. In this study, the author proposes an approach to classifying ALL based on white blood cell images using a convolutional neural network (CNN) model called InceptionV3. The dataset used in this research consists of white blood cell images collected from patients with ALL and healthy individuals. These images were obtained from The Cancer Imaging Archive (TCIA), which is a service that stores large-scale cancer medical images available to the public. During the evaluation phase, the author used training data evaluation metrics such as accuracy and loss to measure the model's performance. The research results show that the InceptionV3 model is capable of classifying white blood cell images with a high level of accuracy. This model achieves an average ALL recognition accuracy of 0.9896 with a loss of 0.031. The use of CNN models such as InceptionV3 in medical image analysis has the potential to improve the efficiency and precision of image-based disease diagnosis.

StainGAN: Learning a structural preserving translation for white blood cell images.

Analysis of white blood cells in blood smear images plays a vital role in computer-aided diagnosis for the analysis and treatment of many diseases. However, different techniques for blood smear preparation result in images with large appearance variations, which limits the performance of large-scale machine learning algorithms. In this paper, we propose StainGAN, an image translation framework to transform the conventional Wright-stained white blood cell images into their rapidly-stained counterpart. Moreover, we designed a cluster-based learning strategy that does not require manual annotations and a multi-scale discriminator that incorporates a richer hierarchy of the spatial context to generate sharper images with better semantic consistency. Experimental results on multiple real-world datasets prove the effectiveness of our proposed framework. Moreover, we show that the transformed images from StainGAN can be used to boost the downstream segmentation performance under the label-limiting scenario.

White Blood Cell Detection Using Yolov8 Integration with DETR to Improve Accuracy

One of the body's most crucial blood cell kinds is the white blood cell. White blood cells, called leukocytes, are crucial for the body's defence mechanism and against hazardous foreign substances, tumour cells, and infectious bacteria. This paper suggests a computer-based automated system for detecting white blood cells using the YOLOV8 transformer and white blood cell analysis in digital images of blood cells. The Generate process uses Yolov8. In Generate, this will produce image processing in the form of annotation results on each type of white blood cell and dataset with COCO format. The DETR Model training conducted in this study is to increase the accuracy value of the white output of the blood cell picture formation. Test results using recall, precision, f1 score and object detection values. In the lymphocyte and basophil datasets, the number of white blood cell images used is only 10 images. Following the results of training from yolov8 using Roboflow, the results were increased relatively high, with an average increase of 0.68 in all five images of white blood cells. This test also gets an average improvement in detection results from Yolo to DETR, getting a fairly significant result of 68%, which is because YOLO cannot handle undetected objects (which are not in the training dataset; furthermore, DETR can handle multiple objects in a single image. Typically, detecting traditional objects such as YOLO requires repeatedly multiple object detection with a fixed batch size

Automatic base-model selection for white blood cell image classification using meta-learning

Healthcare has benefited from the implementation of deep-learning models to solve medical image classification tasks. For example, White Blood Cell (WBC) image analysis is used to diagnose different pathologies like leukemia. However, medical datasets are mostly imbalanced, inconsistent, and costly to collect. Hence, it is difficult to select an adequate model to overcome the mentioned drawbacks. Therefore, we propose a novel methodology to automatically select models to solve WBC classification tasks. These tasks contain images collected using different staining methods, microscopes, and cameras. The proposed methodology includes meta- and base-level learnings. At the meta-level, we implemented meta-models based on prior-models to acquire meta-knowledge by solving meta-tasks using the shades of gray color constancy method. To determine the best models to solve new WBC tasks we developed an algorithm that uses the meta-knowledge and the Centered Kernel Alignment metric. Next, a learning rate finder method is employed to adapt the selected models. The adapted models (base-models) are used in an ensemble learning approach achieving accuracy and balanced accuracy scores of 98.29 and 97.69 in the Raabin dataset; 100 in the BCCD dataset; 99.57 and 99.51 in the UACH dataset, respectively. The results in all datasets outperform most of the state-of-the-art models, which demonstrates our methodology’s advantage of automatically selecting the best model to solve WBC tasks. The findings also indicate that our methodology can be extended to other medical image classification tasks where is difficult to select an adequate deep-learning model to solve new tasks with imbalanced, limited, and out-of-distribution data.

Deep learning for few-shot white blood cell image classification and feature learning

ABSTRACT Differential counting of white blood cells (WBCs) in bone marrow using artificial intelligence (AI)-based models, such as convolutional neural network (CNN) and its various variants, can help physicians to efficiently diagnose many critical diseases such as leukaemia, AIDS and cancers. In this work, we implement a deep transfer learning to several CNN models to examine their effectiveness on automatically classifying WBCs into lymphocytes and non-lymphocytes groups. Our results show that transfer learning can enhance the training of the model and improve the model performance. We also discover that using image masking to remove irrelevant image pixels can further increase the accuracy of the model predictions. Moreover, we assess the impact of three data augmentation techniques to address the imbalance in the data set, which commonly occurs in many biological applications. Our results show that all the three examined data augmentation methods improve the classification results on both training and testing data sets. Altogether, we demonstrate that deep neural networks, when combined with transfer learning and imaging processing techniques, can serve as a powerful tool to conduct automatic differential counting of WBCs, and thus facilitate the diagnosis of the WBC-related disorders, monitor the disease progression and improve the effectiveness of therapeutics.

Hematologic Cancer Detection Using White Blood Cancerous Cells Empowered with Transfer Learning and Image Processing.

Lymphoma and leukemia are fatal syndromes of cancer that cause other diseases and affect all types of age groups including male and female, and disastrous and fatal blood cancer causes an increased savvier death ratio. Both lymphoma and leukemia are associated with the damage and rise of immature lymphocytes, monocytes, neutrophils, and eosinophil cells. So, in the health sector, the early prediction and treatment of blood cancer is a major issue for survival rates. Nowadays, there are various manual techniques to analyze and predict blood cancer using the microscopic medical reports of white blood cell images, which is very steady for prediction and causes a major ratio of deaths. Manual prediction and analysis of eosinophils, lymphocytes, monocytes, and neutrophils are very difficult and time-consuming. In previous studies, they used numerous deep learning and machine learning techniques to predict blood cancer, but there are still some limitations in these studies. So, in this article, we propose a model of deep learning empowered with transfer learning and indulge in image processing techniques to improve the prediction results. The proposed transfer learning model empowered with image processing incorporates different levels of prediction, analysis, and learning procedures and employs different learning criteria like learning rate and epochs. The proposed model used numerous transfer learning models with varying parameters for each model and cloud techniques to choose the best prediction model, and the proposed model used an extensive set of performance techniques and procedures to predict the white blood cells which cause cancer to incorporate image processing techniques. So, after extensive procedures of AlexNet, MobileNet, and ResNet with both image processing and without image processing techniques with numerous learning criteria, the stochastic gradient descent momentum incorporated with AlexNet is outperformed with the highest prediction accuracy of 97.3% and the misclassification rate is 2.7% with image processing technique. The proposed model gives good results and can be applied for smart diagnosing of blood cancer using eosinophils, lymphocytes, monocytes, and neutrophils.

The Effectiveness of Data Augmentation for Mature White Blood Cell Image Classification in Deep Learning — Selection of an Optimal Technique for Hematological Morphology Recognition —

The Effectiveness of Data Augmentation for Mature White Blood Cell Image Classification in Deep Learning — Selection of an Optimal Technique for Hematological Morphology Recognition —