Detection and Classification of White Blood Cells through Deep Learning Techniques

El-Seoud, Samir Abou; Siala, Muaad Hammuda; McKee, Gerard

doi:10.3991/ijoe.v16i15.15481

Cited by 18 publications

(12 citation statements)

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“…Table 4 shows a comparative analysis of the results with those obtained in the state of the art. Our proposed model achieved higher performance than those reported by Abou et al [ 33 ], Baydilli [ 44 ], Banik et al [ 47 ], Huang et al [ 39 ], Jiang et al [ 37 ], Kutlu et al [ 49 ], Liang et al [ 50 ], Özyurt [ 42 ], Patil et al [ 43 ], Togacar et al [ 34 ], Wang et al [ 35 ], Yao et al [ 38 ], and Yu et al [ 51 ], who reported accuracy between 83% and 98%. However, it should be noted that the average performance of our proposal was lower than those reported by Baghel et al [ 45 ] and Basnet et al [ 36 ], where they have included image processing for feature extraction to enhance the prediction performance.…”

Section: Resultscontrasting

confidence: 80%

“…Traditional machine learning (ML) and deep learning (DL) models have been extensively proposed as alternatives for the automatic classification of leukocytes [ 5 , 31 , 32 ]. Such is the case of Abou et al [ 33 ], who developed a CNN model to identify WBC. Likewise, Togacar et al [ 34 ] proposed a subclass separation of WBC images using the AlexNet model.…”

Section: State Of the Artmentioning

confidence: 99%

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An Efficient Multi-Level Convolutional Neural Network Approach for White Blood Cells Classification

et al. 2022

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The evaluation of white blood cells is essential to assess the quality of the human immune system; however, the assessment of the blood smear depends on the pathologist’s expertise. Most machine learning tools make a one-level classification for white blood cell classification. This work presents a two-stage hybrid multi-level scheme that efficiently classifies four cell groups: lymphocytes and monocytes (mononuclear) and segmented neutrophils and eosinophils (polymorphonuclear). At the first level, a Faster R-CNN network is applied for the identification of the region of interest of white blood cells, together with the separation of mononuclear cells from polymorphonuclear cells. Once separated, two parallel convolutional neural networks with the MobileNet structure are used to recognize the subclasses in the second level. The results obtained using Monte Carlo cross-validation show that the proposed model has a performance metric of around 98.4% (accuracy, recall, precision, and F1-score). The proposed model represents a good alternative for computer-aided diagnosis (CAD) tools for supporting the pathologist in the clinical laboratory in assessing white blood cells from blood smear images.

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Section: Resultscontrasting

confidence: 80%

Section: State Of the Artmentioning

confidence: 99%

An Efficient Multi-Level Convolutional Neural Network Approach for White Blood Cells Classification

et al. 2022

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“…On the other hand, the tree structure with a graph in global context for Covid-19 lung segmentation is also presented in [33]. Abou [42] applied convolutional neural network to detect and classify normal white blood cells. Yanni [44] had used hybrid algorithm structure convolutional neural network for segmentation.…”

Section: Literature Reviewmentioning

confidence: 99%

A Deep Bottleneck U-Net Combined With Saliency Map For Classifying Diabetic Retinopathy In Fundus Images

2022

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Early detection of retinopathy plays an important role in the care of people with diabetes. Classification of diabetic retinopathy in fundus images is very challenging because the blood vessels in the retinal images are too small. Morphology of objects with multi-level saliency is the recent choice because of the activation of feature extraction. However, the challenges of the input models are very complex with the blood. The color, lighting or context can become the reasons that create the decline of the primary key for training. This paper proposes a method for classification of diabetic retinopathy using saliency and shape detection of objects based on a deep Bottleneck U-Net (DbU-Net) and support vector machines in retinal blood vessels. The proposed method includes four stages: preprocessing, feature extraction using DbU-Net, saliency prediction and classification based on the support vector machine. To evaluate this method, its results are compared to the results of the other methods by using the same datasets of STARE and DRIVE for testing with evaluation criteria such as sensitivity, specificity, and accuracy. The accuracy of the proposed method is about 97.1% in these datasets. To assess the levels of diabetes, the diagnostician must initially identify the retinal image with diabetes or not. The result of this paper may help the diagnostician to easily do this.

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“…Next, WBC classification and counting is done by [2] which Alexnet has outperform Googlenet and Resnet-101. Other than that, five layers CNN model which contains three layers for feature extraction and the other two layers are used for classification [21]. Lastly, WBC detection and identification using modified LeNet-5 is proposed in [6].…”

Section: Fig 1 5 Types Of Wbcmentioning

confidence: 99%

Cross Validation Analysis of Convolutional Neural Network Variants with Various White Blood Cells Datasets for the Classification Task

2022

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White Blood Cells (WBCs) analysis is an important procedure to detect diseases is that closely related to human immunity system. Manual WBCs analysis is laborious and hence computer aided system (CAD) is a better option to alleviate the shortcoming. Since conventional segmentation-classification approach is tedious to configure, a Convolutional Neural Network (CNN) become recent trend for WBCs classification. Previously, there are many works proposed for WBCs identification. However, the models that can be generalised to works well among various datasets is remain vague. In this paper, an analysis of various CNN models which are simple Alexnet, embedded friendly Mobilenet, inception based Googlenet, systematic architecture VGG-16 and skip connection based model (Resnet & Densenet), are tested with three major WBCs datasets (Kaggle, LISC and IDB-2). From the rigorous experiments, it can be concluded that simple CNN model of Alexnet performs well across all three datasets with 98.08% accuracy on Kaggle, 96.34% accuracy on IDB-2 and 84.52% on LISC. This outcome can be utilise as a basis to improve the CNN classification model that can be generalize to works under various WBCs datasets.

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Detection and Classification of White Blood Cells through Deep Learning Techniques

Cited by 18 publications

References 0 publications

An Efficient Multi-Level Convolutional Neural Network Approach for White Blood Cells Classification

An Efficient Multi-Level Convolutional Neural Network Approach for White Blood Cells Classification

A Deep Bottleneck U-Net Combined With Saliency Map For Classifying Diabetic Retinopathy In Fundus Images

Cross Validation Analysis of Convolutional Neural Network Variants with Various White Blood Cells Datasets for the Classification Task

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