Classification of White Blood Cells Using L-Moments Invariant Features of Nuclei Shape

Al-Dulaimi, Khamael; Nguyen, Kien; Banks, Jasmine; Chandran, Vinod; Tomeo-Reyes, Inmaculada

doi:10.1109/ivcnz.2018.8634678

Cited by 9 publications

(3 citation statements)

References 10 publications

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“…The L-moment measures location, while the L-moment ratio measures the scale, skewness and kurtosis. A hidden layer in the MLP classifier is L-moments that calculation is used from [ 21 ]. Where the data

are in ascending order, and a is the size of the individual projections (the length of the vector used to collect the results of each line integral).…”

Section: Proposed Methodsmentioning

confidence: 99%

“…Different machine learning and Deep Learning (DL) techniques have been proposed widely in different fields, including biomedical and medical images, remote sensing, biometric recognition, health informatics applications and so more. For instance, diagnosis of breast cancer from histopathological images [ 13 ], determination of Autoantibodies against HEp-2 cells (BCA) [ 14 ], medical image analysis [ 15 ], detection of COVID-19 from chest x-ray images [ 16 ], detection of biomedical imaging [ 17 ] white blood cell segmentation [ 18 , 19 ], classification of white blood cells [ 20 , 21 ], and HEp-2 cell segmentation from histopathological images [ 22 ]. Problems related to the classification of HEp-2 cell staining patterns from histopathological images have attracted the attention of many researchers in terms of benchmarking and comparison, and particularly the contests held at international conferences, such as pattern recognition ICPR2012, ICPR2014 and ICPR2016 as well as International Conference Image Processing (ICIP) [ 23 ].…”

Section: Related Workmentioning

confidence: 99%

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Classification of HEp-2 Staining Pattern Images Using Adapted Multilayer Perceptron Neural Network-Based Intra-Class Variation of Cell Shape

Al-Dulaimi

Banks

Al-Sabaawi

et al. 2023

Sensors

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There exists a growing interest from the clinical practice research communities in the development of methods to automate HEp-2 stained cells classification procedure from histopathological images. Challenges faced by these methods include variations in cell densities and cell patterns, overfitting of features, large-scale data volume and stained cells. In this paper, a multi-class multilayer perceptron technique is adapted by adding a new hidden layer to calculate the variation in the mean, scale, kurtosis and skewness of higher order spectra features of the cell shape information. The adapted technique is then jointly trained and the probability of classification calculated using a Softmax activation function. This method is proposed to address overfitting, stained and large-scale data volume problems, and classify HEp-2 staining cells into six classes. An extensive experimental analysis is studied to verify the results of the proposed method. The technique has been trained and tested on the dataset from ICPR-2014 and ICPR-2016 competitions using the Task-1. The experimental results have shown that the proposed model achieved higher accuracy of 90.3% (with data augmentation) than of 87.5% (with no data augmentation). In addition, the proposed framework is compared with existing methods, as well as, the results of methods using in ICPR2014 and ICPR2016 competitions.The results demonstrate that our proposed method effectively outperforms recent methods.

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are in ascending order, and a is the size of the individual projections (the length of the vector used to collect the results of each line integral).…”

Section: Proposed Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Classification of HEp-2 Staining Pattern Images Using Adapted Multilayer Perceptron Neural Network-Based Intra-Class Variation of Cell Shape

Al-Dulaimi

Banks

Al-Sabaawi

et al. 2023

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…Since lighting is difficult to standardize, this will result in color differences in the image 17 The nucleus of WBCs can appear at various sizes in different photographs, and the nucleus can be found in different locations inside cells 16 Since small inter‐class differences exist among continuous stages, the stages of maturation are also complex and challenging in terms of defining distinct standards for each cell type 32 …”

Section: Related Workmentioning

confidence: 99%

Classification of white blood cells based on modified U‐Net and SVM

Balasubramanian¹,

Devi²,

Ramya³

2023

Concurrency and Computation

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SummaryManual investigation of blood cell count is sometimes erroneous due to interoperability error, fatigue error, requiring expert skill and time consuming too. In particular, investigation of white blood cell (WBC) gains importance in identifying diseases like leukemia, leukopenia, etc. WBC does not possess regular structure because they move throughout the blood stream and hence analyzing WBC and its types for structure and shape is quite challenging. To aid in hematology, this work provides classification of WBC classification based on modified U‐Net and support vector machines (SVM). A modified U‐Net architecture is developed to segment WBC followed by feature extraction and classification by radial basis function‐support vector machine (RBF‐SVM). Experiments indicated that the modified U Net segmentation can detect the WBC nucleus with a dice similarity coefficient of 0.972. The proposed U‐Net‐SVM can recognize WBCs in Raabin‐WBC, LISC, and BCCD datasets with an accuracy of 99.45%, 98.62%, and 98.81%, respectively. Further investigation on leukemia dataset, ALL‐IDB2, revealed an accuracy of 99.42% with 100% sensitivity and specificity. The proposed model can be used to investigate WBCs and hence provide a great support to the hematologists in analyzing the blood smear for various disease identifications.

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Automated design for recognition of blood cells diseases from hematopathology using classical features selection and ELM

Khan

Qasim

Lodhi

et al. 2020

Microscopy Res & Technique

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In the human immune system, the white blood cells (WBC) creates bone and lymphoid masses. These cells defend the human body toward several infections, such as fungi and bacteria. The popular WBC types are Eosinophils, Lymphocytes, Neutrophils, and Monocytes, which are manually diagnosis by the experts. The manual diagnosis process is complicated and time‐consuming; therefore, an automated system is required to classify these WBC. In this article, a new method is presented for WBC classification using feature selection and extreme learning machine (ELM). At the very first step, data augmentation is performed to increases the number of images and then implement a new contrast stretching technique name pixel stretch (PS). In the next step, color and gray level size zone matrix (GLSZM) features are calculated from PS images and fused in one vector based on the level of high similarity. However, few redundant features are also included that affect the classification performance. For handling this problem, a maximum relevance probability (MRP) based feature selection technique is implemented. The best‐selected features computed from a fitness function are ELM in this work. All maximum relevance features are put to ELM, and this process is continued until the error rate is minimized. In the end, the final selected features are classified through Cubic SVM. For validation of the proposed method, LISC and Dhruv datasets are used, and it achieved the highest accuracy of 96.60%. From the results, it is clearly shown that the proposed method results are improved as compared to other implemented techniques.

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Classification of White Blood Cells Using L-Moments Invariant Features of Nuclei Shape

Cited by 9 publications

References 10 publications

Classification of HEp-2 Staining Pattern Images Using Adapted Multilayer Perceptron Neural Network-Based Intra-Class Variation of Cell Shape

Classification of HEp-2 Staining Pattern Images Using Adapted Multilayer Perceptron Neural Network-Based Intra-Class Variation of Cell Shape

Classification of white blood cells based on modified U‐Net and SVM

Automated design for recognition of blood cells diseases from hematopathology using classical features selection and ELM

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