A robust algorithm for white blood cell nuclei segmentation

Makem, Mimosette; Tiedeu, Alain; Kom, Guillaume; Nkandeu, Yannick Pascal Kamdeu

doi:10.1007/s11042-022-12285-5

Cited by 9 publications

(5 citation statements)

References 51 publications

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“…The findings show that in TNBC patients, the AI-based digital scores significantly indicate diseasespecific survival. Makem et al [12] proposed an arithmetic operational approach led by a control parameter to detect and extract white blood cell nuclei from blood smear images. Images with a resolution greater than 300 × 300 were first cropped to recover white blood cell nuclei.…”

Section: Literature Reviewmentioning

confidence: 99%

“…However, the CNNs usually suffer from losing essential features, due to which residual blocks are frequently used to address the issue. Recently, additional studies have been put into creating generalized algorithms, resulting in most experts evaluating the techniques using multiple databases [12], [19], [21]. The availability of validation data constitutes one of the significant issues in the healthcare field.…”

Section: Literature Reviewmentioning

confidence: 99%

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An Optimized Multi-Organ Cancer Cells Segmentation for Histopathological Images Based on CBAM-Residual U-Net

Shah

Kang

2023

IEEE Access

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In digital pathology, the accurate segmentation of cell nuclei in histopathology images is essential for medical image analysis. Histopathologists visually evaluate the patterns of cellular architecture and tissue patterns in histopathology image analysis for cancer detection to determine the malignant tissue portions and assess the severity of malignancy. However, manually analyzing scans using a highresolution microscope requires significant effort and time. A computer-assisted diagnosis system utilizing deep learning (DL) algorithms rapidly, reliably, and automatically segments cell nuclei. However, the existing research studies have limited accuracy, high computational costs, and a lack of robustness and generalizability on diverse datasets. To address these issues, this paper proposes a novel and improved DL architecture based on the U-Net, namely, the CBAM-Residual U-Net for improving accuracy, robustness, and generalized segmentation algorithm that can be applied to various staining techniques and tissue structures. The proposed architecture utilizes a ResConv and convolution block attention modules (CBAM). These modules help the proposed architecture learn the image's shallow and deep features. The CBAM module uses an attention mechanism concentrating on essential features such as cell nuclei's shape, texture, and intensity to accurately segment the raw input patterns. The proposed CBAM-Residual U-Net involves fewer trainable parameters, reducing the computational and time costs compared to state-of-the-art techniques. Extensive experiments and comprehensive evaluations are conducted to demonstrate the performance of the proposed scheme on publicly available datasets: i) Data Science Bowl (DSB) 2018, ii) The GlaS, iii) Triple-Negative Breast Cancer (TNBC). The experimental results show that our proposed model considerably outperforms the state-of-the-art techniques and detects cellular boundaries well, providing fine-grained segmentation results.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

An Optimized Multi-Organ Cancer Cells Segmentation for Histopathological Images Based on CBAM-Residual U-Net

Shah

Kang

2023

IEEE Access

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“…Thrombocytes, often known as platelets, are comparatively smaller than erythrocytes and lack a nucleus. Lymphocytes, neutrophils, eosinophils, basophils, and monocytes are the five different types of nucleated cells that make up leukocytes [11]- [13]. Pathologists will be able to identify a variety of blood illnesses and diseases, such as anemia, leukemia, and malaria, with the correct and exact identification, counting, and categorization of WBC and the variation in percentage between the subtypes [14]- [16].…”

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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“…Neutrophils feature multilobed nuclei have two to five lobes each. They are the most prevalent phagocytic cells, accounting for 50%-60% of all WBCs [7][8][9]. Meanwhile, eosinophils account for 1%-6% of all WBCs, with a bilobed nucleus in most cases.…”

Section: Introductionmentioning

confidence: 99%

EfficientNet - XGBoost: An Effective White-Blood-Cell Segmentation and Classification Framework

SivaRao,

Rao

2023

Nano Biomedicine and Engineering

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In the human body, white blood cells (WBCs) are crucial immune cells that help in the early detection of a variety of illnesses. Determination of the number of WBCs can be used to diagnose conditions such as hematological, immunological, and autoimmune diseases, as well as AIDS and leukemia. However, the conventional method of classifying and counting WBCs is time-consuming, laborious, and potentially erroneous. Therefore, this paper presents a computer-assisted automated method for recognizing and detecting WBC categories from blood images. Initially, the blood cell image is preprocessed and then segmented using an effective deep learning architecture called SegNet. Then, the important features are devised and extracted using the EfficientNet architecture. Finally, the WBCs are categorized into four different types using the XGBoost classifier: neutrophils, eosinophils, monocytes, and lymphocytes. The advantages of SegNet, EfficientNet, and XGBoost make the proposed model more robust and achieve a more efficient classification of the WBCs. The BCCD dataset is used to evaluate the performance of the proposed methodology, and the findings are compared to existing state-of-the-art approaches based on accuracy, precision, sensitivity, specificity, and F1-score. Evaluation results show that the proposed approach has a higher rank-1 accuracy of 99.02% and outperformed other existing techniques.

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A robust algorithm for white blood cell nuclei segmentation

Cited by 9 publications

References 51 publications

An Optimized Multi-Organ Cancer Cells Segmentation for Histopathological Images Based on CBAM-Residual U-Net

An Optimized Multi-Organ Cancer Cells Segmentation for Histopathological Images Based on CBAM-Residual U-Net

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

EfficientNet - XGBoost: An Effective White-Blood-Cell Segmentation and Classification Framework

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