ResNeXt-CC: a novel network based on cross-layer deep-feature fusion for white blood cell classification.

Accurate diagnosis of white blood cells from cytopathological images is a crucial step in evaluating leukaemia. In recent years, image classification methods based on fully convolutional networks have drawn extensive attention and achieved competitive performance in medical image classification. In this paper, we propose a white blood cell classification network called ResNeXt-CC for cytopathological images. First, we transform cytopathological images from the RGB color space to the HSV color space so as to precisely extract the texture features, color changes and other details of white blood cells. Second, since cell classification primarily relies on distinguishing local characteristics, we design a cross-layer deep-feature fusion module to enhance our ability to extract discriminative information. Third, the efficient attention mechanism based on the ECANet module is used to promote the feature extraction capability of cell details. Finally, we combine the modified softmax loss function and the central loss function to train the network, thereby effectively addressing the problem of class imbalance and improving the network performance. The experimental results on the C-NMC 2019 dataset show that our proposed method manifests obvious advantages over the existing classification methods, including ResNet-50, Inception-V3, Densenet121, VGG16, Cross ViT, Token-to-Token ViT, Deep ViT, and simple ViT about 5.5-20.43% accuracy, 3.6-23.56% F1-score, 3.5-25.71% AUROC and 8.1-36.98% specificity, respectively.

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.

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.

Leukocytes Classification for Leukemia Detection Using Quantum Inspired Deep Feature Selection.

Leukocytes, also referred to as white blood cells (WBCs), are a crucial component of the human immune system. Abnormal proliferation of leukocytes in the bone marrow leads to leukemia, a fatal blood cancer. Classification of various subtypes of WBCs is an important step in the diagnosis of leukemia. The method of automated classification of WBCs using deep convolutional neural networks is promising to achieve a significant level of accuracy, but suffers from high computational costs due to very large feature sets. Dimensionality reduction through intelligent feature selection is essential to improve the model performance with reduced computational complexity. This work proposed an improved pipeline for subtype classification of WBCs that relies on transfer learning for feature extraction using deep neural networks, followed by a wrapper feature selection approach based on a customized quantum-inspired evolutionary algorithm (QIEA). This algorithm, inspired by the principles of quantum physics, outperforms classical evolutionary algorithms in the exploration of search space. The reduced feature vector obtained from QIEA was then classified with multiple baseline classifiers. In order to validate the proposed methodology, a public dataset of 5000 images of five subtypes of WBCs was used. The proposed system achieves a classification accuracy of about 99% with a reduction of 90% in the size of the feature vector. The proposed feature selection method also shows a better convergence performance as compared to the classical genetic algorithm and a comparable performance to several existing works.

Deep-Learning Based Label-Free Classification of Activated and Inactivated Neutrophils for Rapid Immune State Monitoring.

The differential count of white blood cells (WBCs) is one widely used approach to assess the status of a patient's immune system. Currently, the main methods of differential WBC counting are manual counting and automatic instrument analysis with labeling preprocessing. But these two methods are complicated to operate and may interfere with the physiological states of cells. Therefore, we propose a deep learning-based method to perform label-free classification of three types of WBCs based on their morphologies to judge the activated or inactivated neutrophils. Over 90% accuracy was finally achieved by a pre-trained fine-tuning Resnet-50 network. This deep learning-based method for label-free WBC classification can tackle the problem of complex instrumental operation and interference of fluorescent labeling to the physiological states of the cells, which is promising for future point-of-care applications.

Difference of peripheral blood leucocyte percentages in malaria patients among Sysmex-XE5000,CellaVisionDM96 and manual microscope classification / 国际检验医学杂志

Objective To compare the difference of peripheral blood leucocyte percentages in malaria patients among Sysmex‐XE5000 ,CellaVisionDM96 and manual microscope classification ,and to verify the accuracy and reliability of the Sysmex‐XE5000 automatic blood corpuscle detection instrument for detecting the leukocyte classification in blood routine .Methods The leucocyte percentages data in 82 cases of malaria detected by using the Sysmex‐XE5000 in the Shenzhen Municipal Third People′s Hospital from January 2011 to December 2015 were retrospectively collected ;the peripheral blood smear in 82 cases of malaria obtained the percentages after the classification by the CellaVisionDM 96 ;then the peripheral blood smear was performed the leucocyte classifica‐tion by the manual microscopy for calculating the percentage .Results In the pairwise comparison of percentage obtained from the peripheral blood leucocyte classification by Sysmex‐XE5000 ,CellaVisionDM96 and manual microscopy ,only the monocytes percent‐age had statistical difference between CellaVisionDM 96 and manual microscopy (P0 .05) .Conclusion By comparing the peripheral blood leucocyte percentages data in malaria patients by Sysmex‐XE5000 ,CellaVisionDM96 and manual microscopic classification ,it is indicated that the leukocyte classification data by Sysmex‐XE5000 are accurate and reliable ,malaria parasite does not affect peripheral blood leukocyte classification ,but it is necessary to pay more attention to monocytes classification in CellaVision DM 96 classification .

Performance evaluation of Sysmex XN-9000 automated hematology analyzer / 国际检验医学杂志

Objective To evaluate the performance of Sysmex XN‐9000 automated hematology analyzer .Methods According to international and domestic standards ,performance of analyzer was evaluated .Results The within‐batch and between‐batch preci‐sion ,carryover pollution rate ,linearity range and the accuracy of Sysmex XN‐9000 analyzer were all conform to related require‐ments .Leukocyte classification results compared with manual classification ,the correlation of neutrophil ,lymphocyte ,monocyte and eosinophil were fine ,but correlation of basophil was not very ideal .Conclusion The performance of Sysmex XN‐9000 analyzer could be satisfying ,could meet the needs of clinical inspection and diagnosis and treatment .

Isolation and two-step classification of normal white blood cells in peripheral blood smears.

INTRODUCTION: An automated system for differential white blood cell (WBC) counting based on morphology can make manual differential leukocyte counts faster and less tedious for pathologists and laboratory professionals. We present an automated system for isolation and classification of WBCs in manually prepared, Wright stained, peripheral blood smears from whole slide images (WSI). METHODS: A simple, classification scheme using color information and morphology is proposed. The performance of the algorithm was evaluated by comparing our proposed method with a hematopathologist's visual classification. The isolation algorithm was applied to 1938 subimages of WBCs, 1804 of them were accurately isolated. Then, as the first step of a two-step classification process, WBCs were broadly classified into cells with segmented nuclei and cells with nonsegmented nuclei. The nucleus shape is one of the key factors in deciding how to classify WBCs. Ambiguities associated with connected nuclear lobes are resolved by detecting maximum curvature points and partitioning them using geometric rules. The second step is to define a set of features using the information from the cytoplasm and nuclear regions to classify WBCs using linear discriminant analysis. This two-step classification approach stratifies normal WBC types accurately from a whole slide image. RESULTS: System evaluation is performed using a 10-fold cross-validation technique. Confusion matrix of the classifier is presented to evaluate the accuracy for each type of WBC detection. Experiments show that the two-step classification implemented achieves a 93.9% overall accuracy in the five subtype classification. CONCLUSION: Our methodology achieves a semiautomated system for the detection and classification of normal WBCs from scanned WSI. Further studies will be focused on detecting and segmenting abnormal WBCs, comparison of 20× and 40× data, and expanding the applications for bone marrow aspirates.

Research of automatic peripheral white blood cell classification system / 医疗卫生装备

Identification and classification of white blood cells are important for clinical diagnosis.Many researchers have been seeking the effective methods for white blood cells' automatic classification based on morphological characters.After cell segmentation,leukocytes' feature acquirement and selection,this paper accomplishes white blood cells' automatic classification using Sugeno-model fuzzy neural network and compares the result with that from classifier of BP network.