A self-adaptive approach for white blood cell classification towards point-of-care testing

Dong, Na; Zhai, Meng-die; Chang, Jianfang; Wu, Chun-Ho

doi:10.1016/j.asoc.2021.107709

Cited by 18 publications

(7 citation statements)

References 28 publications

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“…Automatic cell segmentation can be classified into two main routes: unsupervised versus supervised cell segmentation. In unsupervised cell segmentation, traditional approaches in digital images based on boundary thresholding [7,8], clustering [9], a special theory [10], or in combination [11] were mainly utilized to differentiate leukocytes from the backgrounds of erythrocytes. In supervised cell segmentation, both traditional machine learning (e.g., SVM [12] and deep learning (e.g., FCN [13], Mask-RCNN [14] and U-Net [15]) were adopted, which can realize high-quality results of cell segmentation through deep feature extraction and adaptability to large data in an endto-end manner.…”

Section: Introductionmentioning

confidence: 99%

Segmentation, feature extraction and classification of leukocytes leveraging neural networks, a comparative study

Fang,

Huang,

Chen

et al. 2024

Cytometry Pt A

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The gold standard of leukocyte differentiation is a manual examination of blood smears, which is not only time and labor intensive but also susceptible to human error. As to automatic classification, there is still no comparative study of cell segmentation, feature extraction, and cell classification, where a variety of machine and deep learning models are compared with home‐developed approaches. In this study, both traditional machine learning of K‐means clustering versus deep learning of U‐Net, U‐Net + ResNet18, and U‐Net + ResNet34 were used for cell segmentation, producing segmentation accuracies of 94.36% versus 99.17% for the dataset of CellaVision and 93.20% versus 98.75% for the dataset of BCCD, confirming that deep learning produces higher performance than traditional machine learning in leukocyte classification. In addition, a series of deep‐learning approaches, including AlexNet, VGG16, and ResNet18, was adopted to conduct feature extraction and cell classification of leukocytes, producing classification accuracies of 91.31%, 97.83%, and 100% of CellaVision as well as 81.18%, 91.64% and 97.82% of BCCD, confirming the capability of the increased deepness of neural networks in leukocyte classification. As to the demonstrations, this study further conducted cell‐type classification of ALL‐IDB2 and PCB‐HBC datasets, producing high accuracies of 100% and 98.49% among all literature, validating the deep learning model used in this study.

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Section: Introductionmentioning

confidence: 99%

Segmentation, feature extraction and classification of leukocytes leveraging neural networks, a comparative study

Fang,

Huang,

Chen

et al. 2024

Cytometry Pt A

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“…In the traditional WBCs identification system, the classification of WBC from other blood components is difficult due to their similar textures and uneven boundaries. Additionally, WBCs have a complex range of color, structure, form, and intensity [19]- [20]. Different staining and lighting conditions also make it more challenging to identify WBC.…”

Section: Introductionmentioning

confidence: 99%

An Effective WBC Segmentation and Classification Using MobilenetV3–ShufflenetV2 Based Deep Learning Framework

Rao

2023

IEEE Access

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White Blood Cells are essential in keeping track of a person's health. However, the pathologist's experience will determine how the blood smear is evaluated. Furthermore, it is still challenging to classify WBCs accurately because they have various forms, sizes, and colors due to distinct cell subtypes and labeling methods. As a result, a powerful deep learning system for WBC categorization based on MobilenetV3-ShufflenetV2 is described in this research. Initially, the WBC images are segmented using an efficient Pyramid Scene Parsing Network (PSPNet). Following that, MobilenetV3 and an Artificial Gravitational Cuckoo Search (AGCS)-based technique are used to extract and select the global and local features from the segmented images. Finally, the WBC images are divided into five classes using a ShufflenetV2 model. The proposed approach is evaluated on blood cell count and detection (BCCD) and Raabin-Wbc datasets and achieves 99.19% and 99% accuracy, respectively. Moreover, the results are satisfactory when compared to existing algorithms. INDEX TERMS white blood cells, deep learning, MobilenetV3, ShufflenetV2

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“…Lökosit sınıflandırmasının manuel süreci oldukça zaman alıcıdır ve hematoloğun deneyimine bağlıdır. Daha sıklıkla, Lökosit sınıflandırmasını gerçekleştirmek için klinik olarak otomatik bir lökosit analiz cihazı kullanılmaktadır [6] [7]. Bu ticari analizörler, sınıflandırma için segmentasyon ve örüntü tanıma algoritmalarını kullanarak hızlı ve düşük maliyetli analizler gerçekleştirebilmektedir.…”

Section: Introductionunclassified

Modifiye Edilmiş Inception Modülü Kullanılarak İnsan Periferik Kan Hücrelerinin Çoklu Sınıflandırılması

FIRAT

2023

Mühendislik Bilimleri Ve Araştırmaları Dergisi

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Periferik kan hücrelerinin sınıflandırılması anemi ve lösemi gibi birçok kan hastalığının teşhisinde önemli rol oynamaktadır. Bu nedenle, doğru kan hücresi sınıflandırması hastalığın teşhisinde klinik olarak oldukça önemlidir. Son yıllarda, derin öğrenme, özellikle Evrişimsel sinir ağları, güçlü kendi kendine öğrenme yetenekleri sayesinde tıp alanında sıklıkla kullanılmaktadır. Bu çalışmada, kan hücre sınıflandırması için hesaplama maliyetini ve parametre sayısını azaltan derinlemesine ayrılabilir evrişim ile Inception modülünden oluşan yeni bir hibrit yöntem geliştirilmiştir. Bu yöntem, parametre sayısını ve hesaplama maliyetini azaltıp sınıflandırma doğruluğunu arttırmasıyla, standart evrişimsel sinir ağlarına göre bir avantaj sağlamaktadır. Geliştirilen yöntemin performansını test etmek için 8 sınıflı bir kan hücresi veri seti üzerinde yapılan deneysel çalışmalar sonucunda %98.89 doğruluk, %98.88 kesinlik, %98.85 duyarlılık, %98.86 F1-skoru elde edilmiştir. Literatürdeki çalışmalar ile karşılaştırıldığında yöntemimizin etkili olduğu görülmektedir.

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A self-adaptive approach for white blood cell classification towards point-of-care testing

Cited by 18 publications

References 28 publications

Segmentation, feature extraction and classification of leukocytes leveraging neural networks, a comparative study

Segmentation, feature extraction and classification of leukocytes leveraging neural networks, a comparative study

An Effective WBC Segmentation and Classification Using MobilenetV3–ShufflenetV2 Based Deep Learning Framework

Modifiye Edilmiş Inception Modülü Kullanılarak İnsan Periferik Kan Hücrelerinin Çoklu Sınıflandırılması

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