White blood cell classification and counting using convolutional neural network

Macawile, Merl James P.; Quinones, Vonn Vincent; Ballado, Alejandro H.; Cruz, Jennifer C. Dela; Caya, Meo Vincent C.

doi:10.1109/iccre.2018.8376476

Cited by 99 publications

(30 citation statements)

References 16 publications

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“…a good accuracy of all the three models implemented here in classification and counting of the WBCs from 21 sample images. This model obtained a 97.85% specificity, 89.18% sensitivity, and an accuracy of 96.63% [9].…”

Section: White Blood Cell Classification and Counting Usingmentioning

confidence: 89%

A Survey On Automated Methods Used For WBC Classification

Patodia¹,

Nibhasya²,

Chandraprabha³

2020

IJETMS

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The conventional process of the well-known Complete Blood Count test or abbreviated as CBC test has provided pathologists and hematologists with information for diagnosing blood related diseases. This manual method has proved to be tedious, bias and inaccurate. Thus, automated methods for blood cell detection and counting have been sought out to improve the accuracy for better diagnosis. These automated methods lead to early detection of blood related diseases such as anemia, leukemia, lymphoma and other infections in the immune system. To find an alternative to the manual methods, this paper provides a comparative table of 12 papers which were thoroughly surveyed to draw conclusions from their automated methodologies used and final outcomes. This paper can be useful in the future for knowing which methods gave the best and highest accuracies and further advancements can be done based on the inferences made from the literature survey done in this paper. It's just a bird's overview of the diagnosis of blood cells and their applications in the medical field.

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Section: White Blood Cell Classification and Counting Usingmentioning

confidence: 89%

A Survey On Automated Methods Used For WBC Classification

Patodia¹,

Nibhasya²,

Chandraprabha³

2020

IJETMS

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“…Macawile et al [6] propose a method for white blood cell (WBC) classification and counting using pretrained CNNs. They use modified AlexNet, GoogleNet, and ResNet-101 in tandem to obtain classification results.…”

Section: Literature Reviewmentioning

confidence: 99%

Hybrid Inception v3 XGBoost Model for Acute Lymphoblastic Leukemia Classification

Ramaneswaran

Srinivasan

Vincent

et al. 2021

Computational and Mathematical Methods in Medicine

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Acute lymphoblastic leukemia (ALL) is the most common type of pediatric malignancy which accounts for 25% of all pediatric cancers. It is a life-threatening disease which if left untreated can cause death within a few weeks. Many computerized methods have been proposed for the detection of ALL from microscopic cell images. In this paper, we propose a hybrid Inception v3 XGBoost model for the classification of acute lymphoblastic leukemia (ALL) from microscopic white blood cell images. In the proposed model, Inception v3 acts as the image feature extractor and the XGBoost model acts as the classification head. Experiments indicate that the proposed model performs better than the other methods identified in literature. The proposed hybrid model achieves a weighted F1 score of 0.986. Through experiments, we demonstrate that using an XGBoost classification head instead of a softmax classification head improves classification performance for this dataset for several different CNN backbones (feature extractors). We also visualize the attention map of the features extracted by Inception v3 to interpret the features learnt by the proposed model.

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“…al proposed a method that can segment cells from microscopic blood images. The proposed method is based on CNN, which can classify, monocytes, neutrophils, lymphocytes, basophils and eosinophils from a microscopic blood image of Hue Saturation Value [6]. Sahlol et al proposed an advanced hybrid approach to the effective classification of Leukemia.…”

Section: Introductionmentioning

confidence: 99%

Classification of lymphocytes, monocytes, eosinophils, and neutrophils on white blood cells using hybrid Alexnet-GoogleNet-SVM

Çınar

Tuncer

2021

SN Appl. Sci.

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White blood cells (WBC), which form the basis of the immune system, protect the body from foreign invaders and infectious diseases. While the number and structural features of WBCs can provide important information about the health of people, the ratio of the subtypes of these cells and observable deformations are a good indicator in the diagnostic process. The recognition of cells of the type of lymphocytes, neutrophils, eosinophils, basophils and monocytes is critical. In this article, Deep Learning based Hybrid CNN (Convololutional Neural Network) model is proposed for classification of eosinophils, lymphocytes, monocytes, and neutrophils WBCs. The model presented is based on pretrained Alexnet and Googlenet architectures. The feature vector in the last pooling layer of both CNN architectures has been merged, and the resulting feature vector is classified by the Support Vector Machine. To determine the superiority of the proposed method, the classification was also performed and compared using pretrained Alexnet and Googlenet. Hybrid Alexnet-Googlenet-SVM model provides higher accuracy than pretrained Alexnet and Googlenet. The proposed method has been tested with WBC images from Kaggle and LISC database. Accuracy and F1-score were 99.73%, 0.99 and 98.23%, 0.98 for both data sets, respectively.

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White blood cell classification and counting using convolutional neural network

Cited by 99 publications

References 16 publications

A Survey On Automated Methods Used For WBC Classification

A Survey On Automated Methods Used For WBC Classification

Hybrid Inception v3 XGBoost Model for Acute Lymphoblastic Leukemia Classification

Classification of lymphocytes, monocytes, eosinophils, and neutrophils on white blood cells using hybrid Alexnet-GoogleNet-SVM

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