Classification Of White Blood Cells Research Articles

Improving the generalizability of white blood cell classification with few-shot domain adaptation

The morphological classification of nucleated blood cells is fundamental for the diagnosis of hematological diseases. Many Deep Learning algorithms have been implemented to automatize this classification task, but most of the time they fail to classify images coming from different sources. This is known as “domain shift”. Whereas some research has been conducted in this area, domain adaptation techniques are often computationally expensive and can introduce significant modifications to initial cell images. In this article, we propose an easy-to-implement workflow where we trained a model to classify images from two datasets, and tested it on images coming from eight other datasets. An EfficientNet model was trained on a source dataset comprising images from two different datasets. It was afterwards fine-tuned on each of the eight target datasets by using 100 or less-annotated images from these datasets. Images from both the source and the target dataset underwent a color transform to put them into a standardized color style. The importance of color transform and fine-tuning was evaluated through an ablation study and visually assessed with scatter plots, and an extensive error analysis was carried out. The model achieved an accuracy higher than 80% for every dataset and exceeded 90% for more than half of the datasets. The presented workflow yielded promising results in terms of generalizability, significantly improving performance on target datasets, whereas keeping low computational cost and maintaining consistent color transformations. Source code is available at: https://github.com/mc2295/WBC_Generalization

A large multi-focus dataset for white blood cell classification

The White Blood Cell (WBC) differential test ranks as the second most frequently performed diagnostic assay. It requires manual confirmation of the peripheral blood smear by experts to identify signs of abnormalities. Automated digital microscopy has emerged as a solution to reduce this labor-intensive process and improve efficiency. Several publicly available datasets provide various WBC subtypes of differing quality and resolution. These datasets have contributed to advancing WBC classification using machine learning techniques. However, digital microscopy of blood cells with high magnification often requires a wider depth of field, posing challenges for automatic digital microscopy that necessitates capturing multiple stacks of focal planes to obtain complete images of specific blood cells. Our dataset provides 25,773 image stacks from 72 patients. The image labels consist of 18 classes encompassing normal and abnormal cells, with two experts reviewing each label. Each image includes 10 z-stacks of cropped 200 by 200 pixel images, captured using a 50X microscope with 400 nm intervals. This study presents a comprehensive multi-focus dataset for WBC classification.

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.

White blood cell classification network using MobileNetv2 with multiscale feature extraction module and attention mechanism

White blood cells play a crucial role in the human immune system. The accurate classification of white blood cells can help doctors diagnose various diseases for patients. To enhance the classification accuracy of white blood cells micro-vision images, an efficient lightweight deep learning network called ICAFF-MobileNetv2 is proposed in this paper. Firstly, the pruning operations are applied to the MobileNetv2 and the multiscale feature extraction module is proposed to reduce the model size while ensuring the classification accuracy. Secondly, the improved coordinate attention mechanism module and the attention feature fusion module are incorporated into the inverted residual structures to enhance the feature extraction capability and the classification accuracy of the network. Furthermore, considering the imbalance among different types of white blood cells in the datasets, the method of label smoothing is introduced to improve the cross-entropy loss function and further enhance the classification accuracy. Finally, the improved network is validated on the proposed mixed dataset. The experimental results demonstrate that the proposed network features high classification accuracy and small model size. Compared to other classification networks, ICAFF-MobileNetv2 exhibits superior performance with 98.54 %, 98.21 %, 98.56 %, and 98.38 % of the classification accuracy, recall, precision and F1 score respectively, which are 0.7 %, 0.86 %, 0.92 %, and 0.89 % better than MobileNetv2.

Improving Interpretability of Leucocyte Classification with Multimodal Network.

White blood cell classification plays a key role in the diagnosis of hematologic diseases. Models can perform classification either from images or based on morphological features. Image-based classification generally yields higher performance, but feature-based classification is more interpretable for clinicians. In this study, we employed a Multimodal neural network to classify white blood cells, utilizing a combination of images and morphological features. We compared this approach with image-only and feature-only training. While the highest performance was achieved with image-only training, the Multimodal model provided enhanced interpretability by the computation of SHAP values, and revealed crucial morphological features for biological characterization of the cells.

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.

Optimization-based convolutional neural model for the classification of white blood cells

White blood cells (WBCs) are one of the most significant parts of the human immune system, and they play a crucial role in diagnosing the characteristics of pathologists and blood-related diseases. The characteristics of WBCs are well-defined based on the morphological behavior of their nuclei, and the number and types of WBCs can often determine the presence of diseases or illnesses. Generally, there are different types of WBCs, and the accurate classification of WBCs helps in proper diagnosis and treatment. Although various classification models were developed in the past, they face issues like less classification accuracy, high error rate, and large execution. Hence, a novel classification strategy named the African Buffalo-based Convolutional Neural Model (ABCNM) is proposed to classify the types of WBCs accurately. The proposed strategy commences with collecting WBC sample databases, which are preprocessed and trained into the system for classification. The preprocessing phase removes the noises and training flaws, which helps improve the dataset's quality and consistency. Further, feature extraction is performed to segment the WBCs, and African Buffalo fitness is updated in the classification layer for the correct classification of WBCs. The proposed framework is modeled in Python, and the experimental analysis depicts that it achieved 99.12% accuracy, 98.16% precision, 99% sensitivity, 99.04% specificity, and 99.02% f-measure. Furthermore, a comparative assessment with the existing techniques validated that the proposed strategy obtained better performances than the conventional models.

Classification of white blood cells (leucocytes) from blood smear imagery using machine and deep learning models: A global scoping review.

Machine learning (ML) and deep learning (DL) models are being increasingly employed for medical imagery analyses, with both approaches used to enhance the accuracy of classification/prediction in the diagnoses of various cancers, tumors and bloodborne diseases. To date however, no review of these techniques and their application(s) within the domain of white blood cell (WBC) classification in blood smear images has been undertaken, representing a notable knowledge gap with respect to model selection and comparison. Accordingly, the current study sought to comprehensively identify, explore and contrast ML and DL methods for classifying WBCs. Following development and implementation of a formalized review protocol, a cohort of 136 primary studies published between January 2006 and May 2023 were identified from the global literature, with the most widely used techniques and best-performing WBC classification methods subsequently ascertained. Studies derived from 26 countries, with highest numbers from high-income countries including the United States (n = 32) and The Netherlands (n = 26). While WBC classification was originally rooted in conventional ML, there has been a notable shift toward the use of DL, and particularly convolutional neural networks (CNN), with 54.4% of identified studies (n = 74) including the use of CNNs, and particularly in concurrence with larger datasets and bespoke features e.g., parallel data pre-processing, feature selection, and extraction. While some conventional ML models achieved up to 99% accuracy, accuracy was shown to decrease in concurrence with decreasing dataset size. Deep learning models exhibited improved performance for more extensive datasets and exhibited higher levels of accuracy in concurrence with increasingly large datasets. Availability of appropriate datasets remains a primary challenge, potentially resolvable using data augmentation techniques. Moreover, medical training of computer science researchers is recommended to improve current understanding of leucocyte structure and subsequent selection of appropriate classification models. Likewise, it is critical that future health professionals be made aware of the power, efficacy, precision and applicability of computer science, soft computing and artificial intelligence contributions to medicine, and particularly in areas like medical imaging.

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.

SURVEY ON WBC CLASSIFICATION AND DETECTION USING YOLO

Our heart pumps red blood constantly, carrying waste products, hormones, nutrition, oxygen, and other essential health markers. Blood cells in this fluid are made up of many components, including platelets, RBCs, and WBCs. Neutrophils, Lymphocytes, Monocytes, Eosinophils, and Basophils are further subdivided into WBCs. To precisely identify and treat numerous kinds of blood-related illnesses, the detection and analysis of these blood cells is necessary for advanced diagnostics in medicine. Convolutional neural networks (CNNs), for example, are highly relied upon in modern medical diagnostics for the examination of blood cells. The precision and potency of blood cell identification procedures have been greatly improved by CNNs and Additional approaches to deep learning like RNNs and YOLO. The persistence of this research is to list challenges in White Blood cell detection and classification problem. Deep learning-based blood cell identification and classification approaches based on Single Stage approaches. Single Stage Approach involve You only look Once (YOLO). It explores the various architectures for feature selection and classification starting from YoloV1 toV5 and it’s various modifications. In this paper would be looking at things like network parameters, training procedure, and transfer learning application for each variation. It also assesses model complexity, accuracy, performance, generalization, computational efficiency, and transfer learning capabilities, in addition to variables like data amount and source.

A hybrid approach based on multipath Swin transformer and ConvMixer for white blood cells classification

White blood cells (WBC) play an effective role in the body’s defense against parasites, viruses, and bacteria in the human body. Also, WBCs are categorized based on their morphological structures into various subgroups. The number of these WBC types in the blood of non-diseased and diseased people is different. Thus, the study of WBC classification is quite significant for medical diagnosis. Due to the widespread use of deep learning in medical image analysis in recent years, it has also been used in WBC classification. Moreover, the ConvMixer and Swin transformer models, recently introduced, have garnered significant success by attaining efficient long contextual characteristics. Based on this, a new multipath hybrid network is proposed for WBC classification by using ConvMixer and Swin transformer. This proposed model is called Swin Transformer and ConvMixer based Multipath mixer (SC-MP-Mixer). In the SC-MP-Mixer model, firstly, features with strong spatial details are extracted with the ConvMixer. Then Swin transformer effectively handle these features with self-attention mechanism. In addition, the ConvMixer and Swin transformer blocks consist of a multipath structure to obtain better patch representations in the SC-MP-Mixer. To test the performance of the SC-MP-Mixer, experiments were performed on three WBC datasets with 4 (BCCD), 8 (PBC) and 5 (Raabin) classes. The experimental studies resulted in an accuracy of 99.65% for PBC, 98.68% for Raabin, and 95.66% for BCCD. When compared with the studies in the literature and the state-of-the-art models, it was seen that the SC-MP-Mixer had more effective classification results.

Deep transfer learning technique to detect white blood cell classification in regular clinical practice using histopathological images

Deep transfer learning technique to detect white blood cell classification in regular clinical practice using histopathological images

AI-Based Portable White Blood Cells Classification and Counting System in POCT

AI-Based Portable White Blood Cells Classification and Counting System in POCT

White blood cells classification using multi-fold pre-processing and optimized CNN model

White blood cells (WBCs) play a vital role in immune responses against infections and foreign agents. Different WBC types exist, and anomalies within them can indicate diseases like leukemia. Previous research suffers from limited accuracy and inflated performance due to the usage of less important features. Moreover, these studies often focus on fewer WBC types, exaggerating accuracy. This study addresses the crucial task of classifying WBC types using microscopic images. This study introduces a novel approach using extensive pre-processing with data augmentation techniques to produce a more significant feature set to achieve more promising results. The study conducts experiments employing both conventional deep learning and transfer learning models, comparing performance with state-of-the-art machine and deep learning models. Results reveal that a pre-processed feature set and convolutional neural network classifier achieves a significantly better accuracy of 0.99. The proposed method demonstrates superior accuracy and computational efficiency compared to existing state-of-the-art works.

Automatic Classification of White Blood Cells Using a Semi-Supervised Convolutional Neural Network

Automatic Classification of White Blood Cells Using a Semi-Supervised Convolutional Neural Network

Improved Generalization of White Blood Cell Classification by Learnable Illumination Intensity Invariant Layer

Improved Generalization of White Blood Cell Classification by Learnable Illumination Intensity Invariant Layer

WBC YOLO-ViT: 2 Way - 2 stage white blood cell detection and classification with a combination of YOLOv5 and vision transformer

Accurate detection and classification of white blood cells, otherwise known as leukocytes, play a critical role in diagnosing and monitoring various illnesses. However, conventional methods, such as manual classification by trained professionals, must be revised in terms of accuracy, efficiency, and potential bias. Moreover, applying deep learning techniques to detect and classify white blood cells using microscopic images is challenging owing to limited data, resolution noise, irregular shapes, and varying colors from different sources. This study presents a novel approach integrating object detection and classification for numerous type-white blood cell. We designed a 2-way approach to use two types of images: WBC and nucleus. YOLO (fast object detection) and ViT (powerful image representation capabilities) are effectively integrated into 16 classes. The proposed model demonstrates an exceptional 96.449% accuracy rate in classification.

A Deep Learning Framework for Classification of Immature White Blood Cells in Acute Myeloid Leukemia Using Convolutional Autoencoder and Transfer-Learning Ensemble

Acute myeloid leukemia (AML) is a type of acute leukemia that affects myeloid cells. It is characterized by uncontrolled proliferation of immature WBCs causing an accumulation of immature WBCs in the bone marrow and peripheral circulation. AML is an aggressive cancer that can be life-threatening if not treated promptly, therefore, early detection is crucial. Identification of immature WBCs and their subtypes is the first step towards AML diagnosis. Microscopic blood smear image analysis is commonly used to diagnose AML as it is less expensive non-invasive diagnostic tool compared to bone marrow biopsy and immunophenotype. Nonetheless, classifying immature WBCs presents challenges due to their high similarity and minimal interclass variation, especially for intermediate stages of myelopoiesis which make is susceptible to misclassification. The current diagnostic methods of AML are based on manual identification of immature WBCs using peripheral blood smear. These methos are time-consuming, prone to errors, and subject to inter-observer variation. Therefore, this study aims to develop a computer-aided diagnostic framework to accurately identify immature WBCs and classify them into various subtypes. The proposed framework consists of two main components: a binary classification system to classify cells into mature versus immature cells and a multiclassification system to further classify immature cells into four subtypes including myeloblast, monoblast, and promyelocytes. The proposed framework was designed to undergo four distinct phases: the first phase involves preprocessing, which includes data augmentation techniques aimed at addressing the imbalance distribution of WBCs. Augmentation encompasses geometric transformation and generation of synthetic images using convolutional Autoencoder (CAE). Feature extraction phase involves using image embedding and transfer-learning. Image embedding representation was obtained using CAE and then utilized by two pre-trained models: VGG19 and Resnet50. Classification phase was carried out using weighted ensemble of the VGG19 and Resnet50 with the optimal weights determined through Grid search. The validation phase evaluated the model performance using overall accuracy, precision, and sensitivity. The area under the ROC curve (AUC) was used to assess model discrimination ability. The proposed framework demonstrated excellent results, achieving an average accuracy of 99.5%, a sensitivity of 97.97%, and a precision of 94.12%. The model exhibited an excellent discriminatory capability, with an AUC of 99.9%. Furthermore, the proposed framework outperformed previous methods, showcasing improved performance. Key word: Acute myeloid leukemia, peripheral blood smear, immature white blood cells, deep learning, feature extraction, computer-aided diagnosis.

White Blood Cell Classification: Convolutional Neural Network (CNN) and Vision Transformer (ViT) under Medical Microscope

Deep learning (DL) has made significant advances in computer vision with the advent of vision transformers (ViTs). Unlike convolutional neural networks (CNNs), ViTs use self-attention to extract both local and global features from image data, and then apply residual connections to feed these features directly into a fully networked multilayer perceptron head. In hospitals, hematologists prepare peripheral blood smears (PBSs) and read them under a medical microscope to detect abnormalities in blood counts such as leukemia. However, this task is time-consuming and prone to human error. This study investigated the transfer learning process of the Google ViT and ImageNet CNNs to automate the reading of PBSs. The study used two online PBS datasets, PBC and BCCD, and transferred them into balanced datasets to investigate the influence of data amount and noise immunity on both neural networks. The PBC results showed that the Google ViT is an excellent DL neural solution for data scarcity. The BCCD results showed that the Google ViT is superior to ImageNet CNNs in dealing with unclean, noisy image data because it is able to extract both global and local features and use residual connections, despite the additional time and computational overhead.

A-144 Three-wavelength Polarization Imaging Method for the Analysis of Individual Human Blood Cells With Dye-Enhanced Dispersion of Dilution Buffer

Abstract Background Performing basic hematology analysis in a near-patient setting enables more rapid clinical decision making in the process of diagnosis. Traditional blood analyzers in central labs use a complex combination of sensing principles and chemical modification of the sample to derive the necessary parameters for a complete blood count (CBC). To reduce cost and maintenance, varying approaches have been employed to determine a CBC with a single-step measurement technique. One common option is the use of optical microscopy to deliver highly reliable results, examples are fluorescence microscopy, digital holographic microscopy, or spatial light interference microscopy. However, most of these technologies lack a proper determination of cellular-level RBC parameters. Therefore, a non-interferometric, quantitative-phase measurement technique was developed to derive relevant CBC parameters from a single measurement, comparable to a standard lab analyzer. Methods The optical setup employed by this method enables the parallel acquisition of brightfield and differential interference contrast (DIC) images by polarization-sensitive cameras. By using three cameras with individual optical filters, quantitative phase and absorption information can be calculated, which enables the determination of mean cellular volume (MCV), mean cellular hemoglobin (MCH), and mean cellular hemoglobin concentration (MCHC) for red blood cells (RBCs). The whole-blood sample is diluted in a buffer containing a non-cell-penetrating dye to enhance optical refraction contrast and therefore precision in the dependent calculated quantities. The sample is then introduced to a glass flow cell, and the cells are allowed to sediment to the bottom for imaging. Machine learning is applied to the derived images to classify unstained native white blood cells (WBCs) to yield a 5-part differential, which distinguishes between neutrophil, monocyte, lymphocyte, eosinophil and basophil. The algorithm is trained by imaging FACS-validated, purified WBC subgroups. All blood samples are measured on an ADVIA® 2120 Hematology System for control. Results A one-step, optical CBC hematology system based on quantitative phase and absorption image analysis was developed. The correlations for MCV and MCHC compared to a standard lab CBC analyzer were 0.77 and 0.84. The accuracy for the 5-part differential WBC classification was approximately 0.8. To enable a visual inspection of the classification results, a pseudo-colored image of each cell was generated and uploaded in a review tool. Conclusion With a more-refined, less-expensive optical setup, a high-quality CBC analyzer suitable for use at the point of care can be derived with the presented method. A possible simplification could be the use of low-cost but high-resolution Fourier ptychography microscopy (FPM) technology. Imaging of blood samples on a single-cell level offers the potential for the analysis of even more morphological parameters, for example, disease-related changes in the cell membrane. Also, the adhesion behavior of the WBCs in the glass flow cell could indicate cell activation status and therefore give information about infection processes. *Trademark disclaimer: “ADVIA, and all associated marks are trademarks of Siemens Healthcare Diagnostics, Inc. or its affiliates. All other trademarks and brands are the property of their respective owners.”