A Novel Solution of Using Deep Learning for White Blood Cells Classification: Enhanced Loss Function with Regularization and Weighted Loss (ELFRWL)

Basnet, Jaya; Alsadoon, Abeer; Prasad, P. W. C.; Aloussi, Sarmad Al; Alsadoon, Omar Hisham

doi:10.1007/s11063-020-10321-9

Cited by 21 publications

(8 citation statements)

References 32 publications

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“…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. Likewise, the works of Çınar et al [23], Hedge et al [48], and Khan et al [40] have reported accuracy values higher than 99%.…”

Section: Resultscontrasting

confidence: 66%

“…This form makes full use of three-dimensional hyperspectral data for WBC classification. Basnet et al [ 36 ] optimized the WBC CNN classification enhancing loss function with regularization and weighted loss, decreasing time processing. Jiang et al [ 37 ] constructed a new CNN model called WBCNet that can fully extract features of the WBC image by combining the batch normalization algorithm, residual convolution architecture, and improved activation function.…”

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: 66%

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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“…Wang et al, proposed to learn spectral and spatial features from microscopy hyperspectral images using deep convolution networks [21]. A CNN model with loss enhancement with regularization was presented that reduced the processing time [22]. Further, Jiang et al, employed residual convolution structure with batch normalization to improve activation function for enhancing feature extraction in the WBC classification [23].…”

Section: Related Workmentioning

confidence: 99%

White Blood Cell Classification Using Multi-Attention Data Augmentation and Regularization

Bayat

Davey

Coathup

et al. 2022

BDCC

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Accurate and robust human immune system assessment through white blood cell evaluation require computer-aided tools with pathologist-level accuracy. This work presents a multi-attention leukocytes subtype classification method by leveraging fine-grained and spatial locality attributes of white blood cell. The proposed framework comprises three main components: texture-aware/attention map generation blocks, attention regularization, and attention-based data augmentation. The developed framework is applicable to general CNN-based architectures and enhances decision making by paying specific attention to the discriminative regions of a white blood cell. The performance of the proposed method/model was evaluated through an extensive set of experiments and validation. The obtained results demonstrate the superior performance of the model achieving 99.69 % accuracy compared to other state-of-the-art approaches. The proposed model is a good alternative and complementary to existing computer diagnosis tools to assist pathologists in evaluating white blood cells from blood smear images.

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“…The proposed method improves the accuracy rate and loss in both detection and classification of WBCs. The proposed system in [10] consisted of a deep neural convolution network (DCNN) enhanced with a modified loss function besides regularization. The proposed system improved the classification accuracy from 96.1% to 98.92% and a decrease in processing time from 0.354 to 0.216 s. In [11], an automatic approach for WBCs' detection and classification from peripheral blood images was proposed.…”

Section: Related Workmentioning

confidence: 99%

“…Automatic recognition of peripheral blood cells using classical machine-learning approaches has been widely treated in the literature [24,25]. With the emergence of the CNN architectures, many works have been proposed to perform the automatic segmentation and classification of the white blood cells [8][9][10]. The classification of the eight components of the peripheral blood cells was proposed in the paper [22].…”

Section: Influence Of the Training Algorithmmentioning

confidence: 99%

Feature Extraction using CNN for Peripheral Blood Cells Recognition

Ammar¹,

Daho²,

Harrar³

et al. 2018

ICST Transactions on Scalable Information Systems

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INTRODUCTION:The diagnosis of hematological diseases is based on the morphological differentiation of the peripheral blood cell types. OBJECTIVES: In this work, a hybrid model based on CNN features extraction and machine learning classifiers were proposed to improve peripheral blood cell image classification. METHODS: At first, a CNN model composed of four convolution layers and three fully connected layers was proposed. Second, the features from the deeper layers of the CNN classifier were extracted. Third, several models were trained and tested on the data. Moreover, a combination of CNN with traditional machine learning classifiers was carried out. This includes CNN_KNN, CNN_SVM (Linear), CNN_SVM (RBF), and CNN_AdaboostM1. The proposed methods were validated on two datasets. We have used a public dataset containing 12444 images with four types of leukocytes to find the best optimizer function(eosinophil, lymphocyte, monocyte, and neutrophil images). The second dataset contains 17,092 images divided into eight groups: lymphocytes, neutrophils, monocytes). the second public dataset was used to find the best combination of CNN and the machine learning algorithms. the dataset containing 17,092 images: lymphocytes, neutrophils, monocytes, eosinophils, basophils, immature granulocytes, erythroblasts, and platelets. RESULTS: The results reveal that CNN combined with AdaBoost decision tree classifier provided the best performance in terms of cells recognition with an accuracy of 88.8%, demonstrating the performance of the proposed approach. CONCLUSION: The obtained results show that the proposed system can be used in clinical practice.

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A Novel Solution of Using Deep Learning for White Blood Cells Classification: Enhanced Loss Function with Regularization and Weighted Loss (ELFRWL)

Cited by 21 publications

References 32 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

White Blood Cell Classification Using Multi-Attention Data Augmentation and Regularization

Feature Extraction using CNN for Peripheral Blood Cells Recognition

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