White blood cell (WBC) counting analysis in blood smear images using various color segmentation methods

Safuan, Syadia Nabilah Mohd; Tomari, Mohd Razali Md; Zakaria, Wan Nurshazwani Wan

doi:10.1016/j.measurement.2017.11.002

Cited by 74 publications

(48 citation statements)

References 13 publications

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“…The pre-processing step includes image enhancement such as contrast adjustment, noise removal, and image sharpening. All these steps are applied to the input image before image segmentation [41]. There are numerous pre-processing techniques such as median filter, low pass filter, high pass filter, and Gabor filter.…”

Section: A Tml and DL For Leucocytes Classification In Blood Smear Imentioning

confidence: 99%

“…6. Many conventional supervised learning methods have been used to classify leucocytes in microscopic blood smear images, such as Support Vector Machine (SVM) [32][33][34], Naive Bayes (NB) [35][36][37], K-Nearest Neighbor (KNN) [38][39][40], and Artificial Neural Network (ANN) [41][42][43]. Some popular WBCs nuclei detection techniques are identified and reviewed, which are presented in Table 2.…”

Section: A Tml and DL For Leucocytes Classification In Blood Smear Imentioning

confidence: 99%

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A Review on Traditional Machine Learning and Deep Learning Models for WBCs Classification in Blood Smear Images

et al. 2021

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Section: A Tml and DL For Leucocytes Classification In Blood Smear Imentioning

confidence: 99%

Section: A Tml and DL For Leucocytes Classification In Blood Smear Imentioning

confidence: 99%

A Review on Traditional Machine Learning and Deep Learning Models for WBCs Classification in Blood Smear Images

et al. 2021

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“…The classification of five types of white blood cells using neural networks was conducted by Othman et al [13]. Safuan et al investigated white blood cell (WBC) segmentation using various color segmentation methods [14].…”

Section: Introductionmentioning

confidence: 99%

Morphological, Texture, and Color Feature Analysis for Erythrocyte Classification in Thalassemia Cases

et al. 2020

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Abnormal erythrocytes have diverse shapes. The appearance of specific erythrocyte shapes in a person's blood can indicate certain diseases, including thalassemia. We used thalassemia peripheral blood smear images and applied a segmentation process to produce single erythrocyte sub-images. Each erythrocyte has a unique shape. The selection of appropriate features to represent erythrocytes is critical for classification accuracy. We used morphological features such as moment invariants, geometry parameters of the cell and central pallor, and distance angle signature (DAS) morphological features of the cell and central pallor. We combined morphological features with texture and color features to increase the accuracy of erythrocyte classification. In this study, the multi-layer perceptron is used to classify nine shapes of erythrocytes present in thalassemia cases. The experimental results of 7108 erythrocytes indicated an accuracy of 98.11% based on the combination of features. The experimental results also show that the combination of features we proposed produced higher classification accuracy than previous work, which yielded an accuracy of 93.77%.

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“…This process takes time and creates confusion as the result is highly dependent on the pathologist's skills which will yield inaccurate result [9]. Moreover, it will be more challenging as the number of sample increases [10]. However, there is hematology counter in the market which is automated, fast and accurate but it is expensive [11].…”

Section: Introductionmentioning

confidence: 99%

Comparison Performance of Lymphocyte Classification for Various Datasets using Deep Learning

Safuan¹,

Razali²,

Nurshazwani³

et al. 2020

IJACSA

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Analyzing and classifying five types of Lymphocyte White Blood Cell (WBC) is important to monitor the lack or excessive amount of cell in human body. These harmful amount of cell must be detected early for the early treatment can be run to the patient. However, the process may be tedious and time consuming as it is done manually by the experts. Other than that, it may yield inaccurate result as it depends on the pathologist skill and experience. This work presents a way that can be the second opinion to the experts using computer aided system as a solution. Convolutional Neural Network (CNN) is applied to the system to avoid complex structure and to eliminate the features extraction process. Three CNN models of mobilenet, resnet and VGG-16 is experimented on three different datasets which are kaggle, LISC and IDB-2. Kaggle, LISC and IDB-2 dataset consist of 6000, 242 and 260 images respectively. The result is divided into two parts which are dataset and model. As for IDB-2 dataset, the best model is VGG with training and validation accuracy of 0.9721 and 0.7913 respectively. While for kaggle and LISC dataset, the best model is resnet as it achieved training accuracy of 0.9713 and 0.9771 respectively. The highest validation accuracy for kaggle is 0.5955 and 0.5781 for LISC. Lastly, the best database that is most suitable for all model is IDB-2 database. It obtained highest training and validation accuracy for all model of mobilenet, resnet and VGG-16.

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White blood cell (WBC) counting analysis in blood smear images using various color segmentation methods

Cited by 74 publications

References 13 publications

A Review on Traditional Machine Learning and Deep Learning Models for WBCs Classification in Blood Smear Images

A Review on Traditional Machine Learning and Deep Learning Models for WBCs Classification in Blood Smear Images

Morphological, Texture, and Color Feature Analysis for Erythrocyte Classification in Thalassemia Cases

Comparison Performance of Lymphocyte Classification for Various Datasets using Deep Learning

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