Automatic leukocytes classification by distance transform, moment invariant, morphological features, gray level co-occurrence matrices and SVM

Pang, Gai; Zhuang, Yangkai; Zhou, P.

doi:10.2991/icismme-15.2015.231

Cited by 5 publications

(1 citation statement)

References 8 publications

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“…The approach integrates a range of morphological, clustering, and image pre-processing procedures with the utilization of random forest classification. In the study mentioned in the reference, Pang et al ( 2015 ), an automatic leukocyte categorization approach is proposed. Initially, moment invariants are derived using the Euclidean distance transform within the nucleus region, followed by the extraction of morphological characteristics from the segmented cells.…”

Section: Literature Reviewmentioning

confidence: 99%

ALL classification using neural ensemble and memetic deep feature optimization

Awais,

Ahmad,

Kausar

et al. 2024

Front. Artif. Intell.

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Acute lymphoblastic leukemia (ALL) is a fatal blood disorder characterized by the excessive proliferation of immature white blood cells, originating in the bone marrow. An effective prognosis and treatment of ALL calls for its accurate and timely detection. Deep convolutional neural networks (CNNs) have shown promising results in digital pathology. However, they face challenges in classifying different subtypes of leukemia due to their subtle morphological differences. This study proposes an improved pipeline for binary detection and sub-type classification of ALL from blood smear images. At first, a customized, 88 layers deep CNN is proposed and trained using transfer learning along with GoogleNet CNN to create an ensemble of features. Furthermore, this study models the feature selection problem as a combinatorial optimization problem and proposes a memetic version of binary whale optimization algorithm, incorporating Differential Evolution-based local search method to enhance the exploration and exploitation of feature search space. The proposed approach is validated using publicly available standard datasets containing peripheral blood smear images of various classes of ALL. An overall best average accuracy of 99.15% is achieved for binary classification of ALL with an 85% decrease in the feature vector, together with 99% precision and 98.8% sensitivity. For B-ALL sub-type classification, the best accuracy of 98.69% is attained with 98.7% precision and 99.57% specificity. The proposed methodology shows better performance metrics as compared with several existing studies.

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Section: Literature Reviewmentioning

confidence: 99%

ALL classification using neural ensemble and memetic deep feature optimization

Awais,

Ahmad,

Kausar

et al. 2024

Front. Artif. Intell.

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Human peripheral blood leukocyte classification method based on convolutional neural network and data augmentation

Wang

Cao

2019

Medical Physics

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PurposeHuman peripheral blood leukocytes’ classification is important for diagnosing blood diseases. Many microscopic leukocyte image automatic detection methods are proposed. In recent years, convolutional neural networks (CNNs) are applied to microscopic leukocyte image automatic classification. But when a CNN is used for microscopic leukocyte image classification, the dataset’s scarcity and imbalance will lead to low classification accuracy. To improve classification accuracy, a data augmentation method is proposed, and a resampling method is adopted when using a CNN method.MethodsFirst, a deep CNN model for microscopic leukocyte image classification is designed. Then, a new data augmentation method based on feature concentration is proposed to enrich the dataset and overcome the problem of dataset scarcity. To make the CNN model focus on the leukocyte region, many images are generated by putting a segmented leukocyte into images with different microscopic surroundings using an image processing method. Finally, taking the imbalance of the five kinds of leukocytes in the dataset into consideration, a resampling method is adopted. The resampling method iteratively feeds the leukocyte images with a low proportion to the CNN model within an epoch to ensure that images of each of the five kinds of leukocytes are represented in relatively equal numbers in each batch.ResultsThe experimental results demonstrate that the proposed classification method can achieve 97.6% average testing accuracy. Classification precision for the five kinds of leukocytes is above 93.4%, while sensitivity is above 92.5%. Both the proposed data augmentation and the resampling methods improve classification accuracy.ConclusionsA human peripheral blood leukocyte classification method based on a CNN and data augmentation is proposed. The problem of dataset scarcity is solved by the proposed data augmentation method, and the dataset imbalance is solved by a resampling method.

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A computer-assisted human peripheral blood leukocyte image classification method based on Siamese network

Wang

Cao

2020

Med Biol Eng Comput

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Automatic leukocytes classification by distance transform, moment invariant, morphological features, gray level co-occurrence matrices and SVM

Cited by 5 publications

References 8 publications

ALL classification using neural ensemble and memetic deep feature optimization

ALL classification using neural ensemble and memetic deep feature optimization

Human peripheral blood leukocyte classification method based on convolutional neural network and data augmentation

A computer-assisted human peripheral blood leukocyte image classification method based on Siamese network

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