Segmentation and Classification of Leukocytes Using Neural Networks: A Generalization Direction

Rodrigues, Pedro; Ferreira, Manuel; Monteiro, João L.

doi:10.1007/978-3-540-75398-8_17

Cited by 7 publications

(6 citation statements)

References 16 publications

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“…To reduce the 106 problems, they first employed the Back Propagation Neural Network (BPNN) for preclassification and then presented a hybrid model based on the SVM and the pulse-coupled neural network (PCNN). As a result, they looked for ways to lessen the negative effects ( 35 ).…”

Section: Literature Reviewmentioning

confidence: 99%

Using deep DenseNet with cyclical learning rate to classify leukocytes for leukemia identification

Houssein,

Mohamed,

Abdel Samee

et al. 2023

Front. Oncol.

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BackgroundThe examination, counting, and classification of white blood cells (WBCs), also known as leukocytes, are essential processes in the diagnosis of many disorders, including leukemia, a kind of blood cancer characterized by the uncontrolled proliferation of carcinogenic leukocytes in the marrow of the bone. Blood smears can be chemically or microscopically studied to better understand hematological diseases and blood disorders. Detecting, identifying, and categorizing the many blood cell types are essential for disease diagnosis and therapy planning. A theoretical and practical issue. However, methods based on deep learning (DL) have greatly helped blood cell classification.Materials and MethodsImages of blood cells in a microscopic smear were collected from GitHub, a public source that uses the MIT license. An end-to-end computer-aided diagnosis (CAD) system for leukocytes has been created and implemented as part of this study. The introduced system comprises image preprocessing and enhancement, image segmentation, feature extraction and selection, and WBC classification. By combining the DenseNet-161 and the cyclical learning rate (CLR), we contribute an approach that speeds up hyperparameter optimization. We also offer the one-cycle technique to rapidly optimize all hyperparameters of DL models to boost training performance.ResultsThe dataset has been split into two sets: approximately 80% of the data (9,966 images) for the training set and 20% (2,487 images) for the validation set. The validation set has 623, 620, 620, and 624 eosinophil, lymphocyte, monocyte, and neutrophil images, whereas the training set has 2,497, 2,483, 2,487, and 2,499, respectively. The suggested method has 100% accuracy on the training set of images and 99.8% accuracy on the testing set.ConclusionUsing a combination of the recently developed pretrained convolutional neural network (CNN), DenseNet, and the one fit cycle policy, this study describes a technique of training for the classification of WBCs for leukemia detection. The proposed method is more accurate compared to the state of the art.

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Section: Literature Reviewmentioning

confidence: 99%

Using deep DenseNet with cyclical learning rate to classify leukocytes for leukemia identification

Houssein,

Mohamed,

Abdel Samee

et al. 2023

Front. Oncol.

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“…They processed the manners of parametric datasets in a multidimensional limit utilizing Support Vector Machines (SVM). [8] employed an artificial neural web of two steps to verify white blood cells. The first step involved a pre-classification operation of a backpropagation algorithm (BPNN).…”

Section: Introductionmentioning

confidence: 99%

Segmentation White Blood Cells by Machine Learning Algorithms

Mollik¹

2023

World J. Bio. Pharm. Health Sci.

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Blood and its elements have a vital position in human life and are the best indicator for deciding many pathological states. Specifically, white blood cells are of great significance for diagnosing hematological disorders. In this analysis, 350 microscopic blood smudge images have experimented with six machine learning algorithms for the sort of white blood cells, and their renditions have resembled. Thirty-five distinct geometric and statistical (consistency) features have been pulled from blood pictures for practicum and test parameters of machine learning algorithms. According to the outcomes, the Multinomial Logistic Regression (MLR) algorithm accomplished better than the other techniques, with an average of 95% test victory. The MLR can be utilized for the automatic classification of white blood cells. It can be used mainly as a source for diagnosing diseases for hematologists and internal medicine experts.

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“…They analyzed the behavior of parametric datasets in a multidimensional range using Support Vector Machines (SVM). Rodrigues et al (Rodrigues, P., Ferreira, M. & Monteiro, J. 2008) used an artificial neural network consisting of two stages in the classification of white blood cells.…”

Section: Introductionmentioning

confidence: 99%

Classifying White Blood Cells Using Machine Learning Algorithms

Elen

Turan

2019

Uluslararası Muhendislik Arastirma Ve Gelistirme Dergisi

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Blood and its components have an important place in human life and are the best indicator tool in determining many pathological conditions. In particular, the classification of white blood cells is of great importance for the diagnosis of hematological diseases. In this study, 350 microscopic blood smear images were tested with 6 different machine learning algorithms for the classification of white blood cells and their performances were compared. 35 different geometric and statistical (texture) features have been extracted from blood images for training and test parameters of machine learning algorithms. According to the results, the Multinomial Logistic Regression (MLR) algorithm performed better than the other methods with an average 95% test success. The MLR can be used for automatic classification of white blood cells. It can be used especially as a source for diagnosis of diseases for hematologists and internal medicine specialists.

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Segmentation and Classification of Leukocytes Using Neural Networks: A Generalization Direction

Cited by 7 publications

References 16 publications

Using deep DenseNet with cyclical learning rate to classify leukocytes for leukemia identification

Using deep DenseNet with cyclical learning rate to classify leukocytes for leukemia identification

Segmentation White Blood Cells by Machine Learning Algorithms

Classifying White Blood Cells Using Machine Learning Algorithms

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