Detection 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.

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 lightweight white blood cells detection network based on CenterNet and feature fusion modules

White blood cells (WBCs) detection is significant to the diagnosis of many diseases. However, the detection accuracy can be influenced by the significant differences in color, size and morphology of WBCs in the images. In this paper, an improved CenterNet with smaller model size, fewer parameters, and lower computational complexity is proposed for the WBCs accurate detection. Firstly, the lightweight backbone GhostNetv2 is selected to reduce the model size, parameters and computational complexity of the network. Then, a feature pyramid network with the efficient channel attention (ECA) attention mechanism and the multi-scale feature extraction modules is constructed to enhance the capability of feature extraction and focus more on WBCs. Finally, the heatmap loss function is modified by proposing an improved mean squared error loss function to enhance the fitting ability between the predicted values and the ground truth of the heatmap. Experimental results show that the model size of the proposed lightweight CenterNet is only 19.9 MB, and the mAP.5 is 97.36%. The model size is reduced by 84% while the mAP.5 and FPS are increased by 0.7% and 10.4 compared to the original CenterNet. Moreover, the detection accuracy of the proposed lightweight CenterNet is comparable to the existing mainstream networks and its detection performance on different datasets is good, while the model size, parameters and computational complexity of the network is significantly reduced, and can be used for WBCs detection effectively.

DWS-YOLO: A Lightweight Detector for Blood Cell Detection

ABSTRACT Peripheral blood cell detection is an essential component of medical practice and is used to diagnose and treat diseases, as well as to monitor the progress of therapies. Our objective is to construct an efficient deep learning model for peripheral blood cell analysis that achieves an optimized balance between inference speed, computational complexity, and detection accuracy. In this article, we propose the DWS-YOLO blood detector, which is a lightweight blood detector. Our model includes several improved modules, including the lightweight C3 module, the increased combined attention mechanism, the Scylla-IoU loss function, and the improved soft non-maximum suppression. Improved attention, loss function, and suppression enhance detection accuracy, while lightweight C3 module reduces computation time. The experiment results demonstrate that our proposed modules can enhance a detector’s detection performance, and obtain new state-of-the-art (SOTA) results and excellent robustness performance on the BCCD dataset. On the white blood cell detection dataset (Raabin-WBC), the proposed detector’s generalization performance was confirmed to be satisfactory. Our proposed blood detector achieves high detection accuracy while requiring few computational resources and is very suitable for resource-limited but efficient medical device environments, providing a reliable and advanced solution for blood detection that greatly improves the efficiency and effectiveness of peripheral blood cell analysis in clinical practice.

An intelligent white blood cell detection and multi-class classification using fine optimal DCRNet

An intelligent white blood cell detection and multi-class classification using fine optimal DCRNet

Efficient leukocytes detection and classification in microscopic blood images using convolutional neural network coupled with a dual attention network

Leukocytes, also called White Blood Cells (WBCs) or leucocytes, are the cells that play a pivotal role in human health and are vital indicators of diseases such as malaria, leukemia, AIDS, and other viral infections. WBCs detection and classification in blood smears offers insights to pathologists, aiding diagnosis across medical conditions. Traditional techniques, including manual counting, detection, classification, and visual inspection of microscopic images by medical professionals, pose challenges due to their labor-intensive nature. However, traditional methods are time consuming and sometimes susceptible to errors. Here, we propose a high-performance convolutional neural network (CNN) coupled with a dual-attention network that efficiently detects and classifies WBCs in microscopic thick smear images. The main aim of this study was to enhance clinical hematology systems and expedite medical diagnostic processes. In the proposed technique, we utilized a deep convolutional generative adversarial network (DCGAN) to overcome the limitations imposed by limited training data and employed a dual attention mechanism to improve accuracy, efficiency, and generalization. The proposed technique achieved overall accuracy rates of 99.83%, 99.35%, and 99.60% for the peripheral blood cell (PBC), leukocyte images for segmentation and classification (LISC), and Raabin-WBC benchmark datasets, respectively. Our proposed approach outperforms state-of-the-art methods in terms of accuracy, highlighting the effectiveness of the strategies employed and their potential to enhance diagnostic capabilities and advance real-world healthcare practices and diagnostic systems.

LAFSSD: lightweight and advanced FSSD for multi-scale detection of platelets and white blood cells in human peripheral blood smear images

LAFSSD: lightweight and advanced FSSD for multi-scale detection of platelets and white blood cells in human peripheral blood smear images

White Blood Cell Type Detection Using K-means Clustering and Multicolor Spaces

The white blood cell (WBC) plays an instrumental role in protecting the human body from different infectious diseases and unknown biological invaders. Their count and cellular morphology drastically change when the body suffers from any such disease or abnormal conditions. This paper demonstrates the detection of five types of WBC present in human blood smear images. Several authors proposed WBC detection using only one color space. But this paper addresses WBC-type detection using four popular color spaces – RGB (red, green, and blue), HSV (hue, saturation, and intensity), YCbCr (luminance, chroma blue, and chroma red), and L*a*b* (luminosity, chromaticity layer “a*”, and chromaticity layer “b*”). The proposed methodology involves color thresholding, K-means clustering, nuclear region analysis, and the implementation of a feedforward neural network. The unsupervised machine learning algorithm, K-means clustering is used to segment nuclei from the blood smear images. The detection of WBC-type using four color spaces is compared in terms of accuracy, sensitivity, specificity, and positive predictive value. It is observed that HSV-based WBC-type detection methodology outperforms methodologies based on RGB, YCbCr, and L*a*b* color spaces. The same methodology mentioned above is then compared with the works proposed by other authors.

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.

Alternative strategies to provide actionable results when supply of urinalysis strips is unavailable

Abstract Urinalysis “dip strips” offer physicians qualitative results of 6 analytes which are immediately actionable: protein, glucose, leukocyte esterase, nitrates, blood, and ketones; however, the strips are instrument specific such that they are at the mercy of supply chain shortages. Due to vendor supply issues, we implemented a strategy that would continue to support clinical decision making in the absence of supplies for protein, glucose, white blood cells (WBC) and blood (RBC) detection. Method: In supply shortage situations, an automated algorithm triggered such that urine samples would be sent to the automation chemistry line to run quantification of protein and glucose and make sure an automated urine microscopy was performed for RBC and WBC detection. The algorithm triggered printing two labels so nursing would collect two specimens. We monitored the turn-around time from the specimen being received in the laboratory to results, made sure that the culture reflex order was triggered, and tracked complaints by physicians regarding not having all usual urinalysis results provided by the strips. Prior to implementation, correlation between sample types for protein and glucose measurement was found acceptable. RESULTS: When we put in place the algorithm, the turn-around time of urine microscopic study was identical to that obtained when the urinalysis was done with the strips; however, the quantification of glucose and protein took approximately 30 minutes more. Urine reflex cultures were triggered correctly with the algorithm as they derive entirely from a WBC count >20. During the period we had the algorithm in place we only had one complaint by a physician wanting to have results of nitrates. Conclusion: The successful implementation of the algorithm allowed the laboratory to provide actionable urinalysis analytes in a timely manner with minimal complaints from physicians.

Automatic myeloblast segmentation in acute myeloid leukemia images based on adversarial feature learning

The diagnosis of acute myeloid leukemia (AML) requires reliable detection and counting of immature white blood cells called myeloblasts on bone marrow aspirate and biopsy slides. Experienced hematologists/oncologists typically spend many hours manually reviewing blood cells and performing a microscopic morphological examination of myeloblasts on glass slides to determine this diagnosis. While manual counting of myeloblasts is considered the gold standard for an accurate diagnosis of AML, the process is time-consuming and difficult to standardize due to high intra- and inter-observer variability. Machine learning algorithms coupled with deep learning techniques represent potentially promising approaches for automated counting. Since myeloblasts possess very similar texture patterns to other hematopoietic cells (OHCs), accurately differentiating myeloblasts from other blood cells is a challenge for most segmentation models. We present a new conditional generative adversarial network (cGAN)-based segmentation model (AMLcGAN) to efficiently segment myoblasts from slides of patients with AML. Validation of the approach on 204 cytological images reveals a mean dice coefficient (mDICE) of 82.51 % with respect to manual segmentations. We also compared the new AMLcGAN method with three deep learning models: Unet, Segnet and Pix2pix, the corresponding mDICE values were 77.4 %, 78.2 %, and 79.53 %, respectively. We also employed the segmentation results from 4 different models to extract blast statistics feature, Haralick texture feature, Fractal dimension feature and myeloblast shape features to create 4 different prognostic models to predict risk of recurrence in AML patients. TheConcordance Index values for AMLcGAN and the three other models respectively were 0.63, 0.53, 0.58, and 0.56. The comprehensive evaluation results presented suggest that the AMLcGAN approach can enable accurate blast segmentation and for construction of accurate prognostic models for predicting risk of recurrence in AML patients.

Red Deer Optimization with Deep Learning based Robust White Blood Cell Detection and Classification Model

The use of deep learning techniques for White Blood Cell (WBC) classification has garnered significant attention on medical image analysis due to its potential to automate and enhance the accuracy of WBC classification, which is critical for disease diagnosis and infection detection. Convolutional neural networks (CNNs) have revolutionized image analysis tasks, including WBC classification effectively capturing intricate spatial patterns and distinguishing between different cell types. A key advantage of deep learning-based WBC classification is its capability to handle large datasets, enabling models to learn the diverse variations and characteristics of different cell types. This facilitates robust generalization and accurate classification of previously unseen samples. In this paper, a novel approach called Red Deer Optimization with Deep Learning for Robust White Blood Cell Detection and Classification was presented. The proposed model incorporates various components to improve performance and robustness. Image pre-processing involves the utilization of median filtering, while U-Net++ is employed for segmentation, facilitating accurate delineation of WBCs. Feature extraction is performed using the Xception model, which effectively captures informative representations of the WBCs. For classification, BiGRU model is employed, leveraging its ability to model temporal dependencies in the WBC sequences. To optimize the performance of the BiGRU model, the RDO is utilized for parameter tuning, resulting in enhanced accuracy and faster convergence of the deep learning models. The integration of RDO contributes to more reliable detection and classification of WBCs, further improving the overall performance and robustness of the approach. Experimental results demonstrate the superiority of our Red Deer Optimization with deep learning-based approach over traditional methods and standalone deep learning models in achieving robust WBC detection and classification. This research highlights the possibility of combining deep learning techniques with optimization algorithms for improving WBC analysis, offering valuable insights for medical professionals and medical image analysis.

Peripheral Blood Leukocyte Detection Based on an Improved Detection Transformer Algorithm.

The combination of a blood cell analyzer and artificial microscopy to detect white blood cells is used in hospitals. Blood cell analyzers not only have large throughput, but they also cannot detect cell morphology; although artificial microscopy has high accuracy, it is inefficient and prone to missed detections. In view of the above problems, a method based on Fourier ptychographic microscopy (FPM) and deep learning to detect peripheral blood leukocytes is proposed in this paper. Firstly, high-resolution and wide-field microscopic images of human peripheral blood cells are obtained using the FPM system, and the cell image data are enhanced with DCGANs (deep convolution generative adversarial networks) to construct datasets for performance evaluation. Then, an improved DETR (detection transformer) algorithm is proposed to improve the detection accuracy of small white blood cell targets; that is, the residual module Conv Block in the feature extraction part of the DETR network is improved to reduce the problem of information loss caused by downsampling. Finally, CIOU (complete intersection over union) is introduced as the bounding box loss function, which avoids the problem that GIOU (generalized intersection over union) is difficult to optimize when the two boxes are far away and the convergence speed is faster. The experimental results show that the mAP of the improved DETR algorithm in the detection of human peripheral white blood cells is 0.936. In addition, this algorithm is compared with other convolutional neural networks in terms of average accuracy, parameters, and number of inference frames per second, which verifies the feasibility of this method in microscopic medical image detection.

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

An Explainable Vision Transformer Model Based White Blood Cells Classification and Localization.

White blood cells (WBCs) are crucial components of the immune system that play a vital role in defending the body against infections and diseases. The identification of WBCs subtypes is useful in the detection of various diseases, such as infections, leukemia, and other hematological malignancies. The manual screening of blood films is time-consuming and subjective, leading to inconsistencies and errors. Convolutional neural networks (CNN)-based models can automate such classification processes, but are incapable of capturing long-range dependencies and global context. This paper proposes an explainable Vision Transformer (ViT) model for automatic WBCs detection from blood films. The proposed model uses a self-attention mechanism to extract features from input images. Our proposed model was trained and validated on a public dataset of 16,633 samples containing five different types of WBCs. As a result of experiments on the classification of five different types of WBCs, our model achieved an accuracy of 99.40%. Moreover, the model's examination of misclassified test samples revealed a correlation between incorrect predictions and the presence or absence of granules in the cell samples. To validate this observation, we divided the dataset into two classes, Granulocytes and Agranulocytes, and conducted a secondary training process. The resulting ViT model, trained for binary classification, achieved impressive performance metrics during the test phase, including an accuracy of 99.70%, recall of 99.54%, precision of 99.32%, and F-1 score of 99.43%. To ensure the reliability of the ViT model's, we employed the Score-CAM algorithm to visualize the pixel areas on which the model focuses during its predictions. Our proposed method is suitable for clinical use due to its explainable structure as well as its superior performance compared to similar studies in the literature. The classification and localization of WBCs with this model can facilitate the detection and reporting process for the pathologist.

Rapid detection of white blood cells using hyperspectral microscopic imaging system combined with Multi-data Faster RCNN

Blood test is a basic medical test item, and the content of white blood cells (WBCs) is directly related to the evaluation of the patient's health and the judgment of serious diseases. However, both manual detection method and traditional image recognition method have the problem of insufficient efficiency and accuracy. The purpose of this study is to propose a fast and accurate WBC detection method, called hyperspectral based Multi-data Faster RCNN. For hyperspectral multi-dimensional sensing information, one-dimensional (1-D) CNN and Faster RCNN were used to model and analyze spectral and image features, respectively. And then, the Multi-data Faster RCNN was built, in which the 1-D CNN network and fusion network layer were added in Faster RCNN to realize the joint detection based on spectral and image fusion features of WBCs. The hyperspectral system coupled with Multi-data Faster RCNN can effectively improve the accuracy of WBCs’ recognition. The average precision (Ap) value and the F1 score of each classification of WBCs were above 0.9, and the mean average precision (mAP) even reached 0.962. The results showed that the Multi-data Faster RCNN integrated with hyperspectral imaging had great potential in the detection of WBCs.

Medical Image Classifications for 6G IoT-Enabled Smart Health Systems

As day-to-day-generated data become massive in the 6G-enabled Internet of medical things (IoMT), the process of medical diagnosis becomes critical in the healthcare system. This paper presents a framework incorporated into the 6G-enabled IoMT to improve prediction accuracy and provide a real-time medical diagnosis. The proposed framework integrates deep learning and optimization techniques to render accurate and precise results. The medical computed tomography images are preprocessed and fed into an efficient neural network designed for learning image representations and converting each image to a feature vector. The extracted features from each image are then learned using a MobileNetV3 architecture. Furthermore, we enhanced the performance of the arithmetic optimization algorithm (AOA) based on the hunger games search (HGS). In the developed method, named AOAHG, the operators of the HGS are applied to enhance the AOA's exploitation ability while allocating the feasible region. The developed AOAG selects the most relevant features and ensures the overall model classification improvement. To assess the validity of our framework, we conducted evaluation experiments on four datasets, including ISIC-2016 and PH2 for skin cancer detection, white blood cell (WBC) detection, and optical coherence tomography (OCT) classification, using different evaluation metrics. The framework showed remarkable performance compared to currently existing methods in the literature. In addition, the developed AOAHG provided results better than other FS approaches according to the obtained accuracy, precision, recall, and F1-score as performance measures. For example, AOAHG had 87.30%, 96.40%, 88.60%, and 99.69% for the ISIC, PH2, WBC, and OCT datasets, respectively.

White blood cell segmentation by using vector color approach

White blood cells play an important role in protecting the body from various types of diseases, therefore blood tests are done to determine a person’s disease. Diagnosis of blood diseases is done by analyzing and counting the white blood cells in the blood. Optimal calculations and analysis of white blood cell are important, so research on white blood cell detection is a rapidly growing topic. Many detection methods are proposed using the existing segmentation techniques. In this paper, the detection method is proposed using vector operation approach instead general segmentation technique. From the experiments performed, it is shown that the proposed method can detect white blood cells on smear images. Further, this method extracts the nucleus from white blood cells well.

One-stage and lightweight CNN detection approach with attention: Application to WBC detection of microscopic images

White blood cell (WBC) detection in microscopic images is indispensable in medical diagnostics; however, this work, based on manual checking, is time-consuming, labor-intensive, and easily results in errors. Using object detectors for WBCs with deep convolutional neural networks can be regarded as a feasible solution. In this paper, to improve the examination precision and efficiency, a one-stage and lightweight CNN detector with an attention mechanism for detecting microscopic WBC images, and a white blood cell detection vision system are proposed. The method integrates different optimizing strategies to strengthen the feature extraction capability through the combination of an improved residual convolution module, hybrid spatial pyramid pooling module, improved coordinate attention mechanism, efficient intersection over union (EIOU) loss and Mish activation function. Extensive ablation and contrast experiments on the latest public Raabin-WBC dataset verify the effectiveness and robustness of the proposed detector for achieving a better overall detection performance. It is also more efficient than other existing studies for blood cell detection on two additional classic public BCCD and LISC datasets. The novel detection approach is significant and flexible for medical technicians to use for blood cell microscopic examination in clinical practice.

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.