ROBUST DISCRIMINATION OF LEUKOCYTES PROTUBERANT TYPES FOR EARLY DIAGNOSIS OF LEUKEMIA

Advanced laboratory technology has made blood testing more automated, robust and tests are being implemented comprehensively. Leukocytes type differentiation is a critical hematological images analysis step of blood film as it delivers valuable information in diagnosis of several diseases. At present, the morphological examination of leukocytes is done manually and this process is very tedious, inefficient and slow. Although many white blood cells detection or classification techniques are presented by different researchers, there is still a need of fully automated and an efficient detection system of blood cells with its particular types for an early diagnosis of leukemia. This paper presents a technique for the classification of protuberant types of leukocytes and early diagnosis of leukemia. The work is divided into the following main stages: (a) image augmentation, (b) wavelet composition and decomposition for attaining high and low frequency bands of the cell image, (c) convolutional neural network (CNN) training model for the classification of leukocytes categories and (d) prediction of leukemia. The main intention behind this study is to develop an automated, robust and efficient classification and detection system of leukocytes for microscopic blood images.

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Automatic classification of cells in microscopic fecal images using convolutional neural networks

Bioscience Reports ◽

10.1042/bsr20182100 ◽

2019 ◽

Vol 39 (4) ◽

Cited By ~ 3

Author(s):

Xiaohui Du ◽

Lin Liu ◽

Xiangzhou Wang ◽

Guangming Ni ◽

Jing Zhang ◽

...

Keyword(s):

Blood Cells ◽

Detection Efficiency ◽

Detection System ◽

White Blood Cells ◽

Principal Component ◽

Visible Image ◽

Single Target ◽

Extraction Algorithm ◽

Detection Schemes

Abstract The analysis of fecal-type components for clinical diagnosis is important. The main examination involves the counting of red blood cells (RBCs), white blood cells (WBCs), and molds under the microscopic. With the development of machine vision, some vision-based detection schemes have been proposed. However, these methods have a single target for detection, with low detection efficiency and low accuracy. We proposed an algorithm to identify the visible image of fecal composition based on intelligent deep learning. The algorithm mainly includes region proposal and candidate recognition. In the process of segmentation, we proposed a morphology extraction algorithm in a complex background. As for the candidate recognition, we proposed a new convolutional neural network (CNN) architecture based on Inception-v3 and principal component analysis (PCA). This method achieves high-average Precision of 90.7%, which is better than the other mainstream CNN models. Finally, the images within the rectangle marks were obtained. The total time for detection of an image was roughly 1200 ms. The algorithm proposed in the present paper can be integrated into an automatic fecal detection system.

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Classification of White Blood Cells Based on Cell Diameter, Specific Membrane Capacitance and Cytoplasmic Conductivity Leveraging Microfluidic Constriction Channel

2021 21st International Conference on Solid-State Sensors, Actuators and Microsystems (Transducers) ◽

10.1109/transducers50396.2021.9495490 ◽

2021 ◽

Author(s):

Huiwen Tan ◽

Minruihong Wang ◽

Yi Zhang ◽

Xukun Huang ◽

Deyong Chen ◽

...

Keyword(s):

Blood Cells ◽

White Blood Cells ◽

Membrane Capacitance ◽

Cell Diameter ◽

Specific Membrane

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New Segmentation and Feature Extraction Algorithm for the Classification of White Blood Cells in Peripheral Smear Images

10.1101/2021.04.29.441751 ◽

2021 ◽

Author(s):

Eslam Tavakoli ◽

Ali Ghaffari ◽

Seyedeh-Zahra Mousavi Kouzehkanan ◽

Reshad Hosseini

Keyword(s):

Blood Cells ◽

White Blood Cells ◽

Dice Similarity Coefficient ◽

Second Phase ◽

Peripheral Smear ◽

Color Features ◽

Svm Model ◽

Extraction Algorithm ◽

Processing Techniques

This article addresses a new method for classification of white blood cells (WBCs) using image processing techniques and machine learning methods. The proposed method consists of three steps: detecting the nucleus and cytoplasm, extracting features, and classification. At first, a new algorithm is designed to segment the nucleus. For the cytoplasm to be detected, only a part of it which is located inside the convex hull of the nucleus is involved in the process. This attitude helps us overcome the difficulties of segmenting the cytoplasm. In the second phase, three shape and four novel color features are devised and extracted. Finally, by using an SVM model, the WBCs are classified. The segmentation algorithm can detect the nucleus with a dice similarity coefficient of 0.9675. The proposed method can categorize WBCs in Raabin-WBC, LISC, and BCCD datasets with accuracies of 94.47 %, 92.21 %, and 94.20 %, respectively. It is worth mentioning that the hyperparameters of the classifier are fixed only with Raabin-WBC dataset, and these parameters are not readjusted for LISC and BCCD datasets. The obtained results demonstrate that the proposed method is robust, fast, and accurate.

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Human-level recognition of blast cells in acute myeloid leukemia with convolutional neural networks

10.1101/564039 ◽

2019 ◽

Cited By ~ 1

Author(s):

Christian Matek ◽

Simone Schwarz ◽

Karsten Spiekermann ◽

Carsten Marr

Keyword(s):

Acute Myeloid Leukemia ◽

Blood Cells ◽

Myeloid Leukemia ◽

White Blood Cells ◽

Cell Types ◽

Hematologic Malignancies ◽

Malignant Cell ◽

Morphological Examination ◽

Blast Cells ◽

Acute Myeloid

AbstractReliable recognition of malignant white blood cells is a key step in the diagnosis of hematologic malignancies such as Acute Myeloid Leukemia. Microscopic morphological examination of blood cells is usually performed by trained human examiners, making the process tedious, time-consuming and hard to standardise.We compile an annotated image dataset of over 18,000 white blood cells, use it to train a convolutional neural network for leukocyte classification, and evaluate the network’s performance. The network classifies the most important cell types with high accuracy. It also allows us to decide two clinically relevant questions with human-level performance, namely (i) if a given cell has blast character, and (ii) if it belongs to the cell types normally present in non-pathological blood smears.Our approach holds the potential to be used as a classification aid for examining much larger numbers of cells in a smear than can usually be done by a human expert. This will allow clinicians to recognize malignant cell populations with lower prevalence at an earlier stage of the disease.

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Klasifikasi Sel Darah Putih dan Sel Limfoblas Menggunakan Metode Multilayer Perceptron Backpropagation

IJEIS (Indonesian Journal of Electronics and Instrumentation Systems) ◽

10.22146/ijeis.49943 ◽

2019 ◽

Vol 9 (2) ◽

pp. 173

Author(s):

Apri Nur Liyantoko ◽

Ika Candradewi ◽

Agus Harjoko

Keyword(s):

Blood Cell ◽

White Blood Cell ◽

Blood Cells ◽

White Blood Cells ◽

Back Propagation ◽

Cell Types ◽

Diagnostic Process ◽

The Subject ◽

Processing Techniques

Leukemia is a type of cancer that is on white blood cell. This disease are characterized by abundance of abnormal white blood cell called lymphoblast in the bone marrow. Classification of blood cell types, calculation of the ratio of cell types and comparison with normal blood cells can be the subject of diagnosing this disease. The diagnostic process is carried out manually by hematologists through microscopic image. This method is likely to provide a subjective result and time-consuming.The application of digital image processing techniques and machine learning in the process of classifying white blood cells can provide more objective results. This research used thresholding method as segmentation and multilayer method of back propagation perceptron with variations in the extraction of textural features, geometry, and colors. The results of segmentation testing in this study amounted to 68.70%. Whereas the classification test shows that the combination of feature extraction of GLCM features, geometry features, and color features gives the best results. This test produces an accuration value 91.43%, precision value of 50.63%, sensitivity 56.67%, F1Score 51.95%, and specitifity 94.16%.

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The prevalence of leukocyte abnormalities among Sudanese patients with sickle cell disease

World Journal of Advanced Research and Reviews ◽

10.30574/wjarr.2021.9.1.0026 ◽

2021 ◽

Vol 9 (1) ◽

pp. 262-267

Author(s):

Tarig Osman Khalafallah Ahmed ◽

Ekhlas Alrasheid Abu Elfadul ◽

Ahmed A. Agab Eldour ◽

Omer Ibrahim Abdallah Mohammed

Keyword(s):

Sickle Cell Disease ◽

Cell Volume ◽

Sickle Cell ◽

Blood Cells ◽

White Blood Cells ◽

Blood Film ◽

Cell Disease ◽

Mean Cell Volume ◽

Blood Disorder ◽

Age Range

Sickle cell disease (SCD) is an inherited blood disorder that affects red blood cells. The study was conducted in Elobied town during the period May 2011 to September 2011. The aim of this study is to detect the abnormalities of leucocytes among sickle cell anemic patients. 40 sickle cell anemic patients; age range between 8 months to 23 years. Blood sample was taken for all patients and the laboratory investigation were performed using automated estimation for: hemoglobin (Hb), Packed cell volume (PCV), red cell count (RBCs), mean cell volume (MCV), mean cell hemoglobin (MCH), mean cell concentration (MCHC), and total white blood cells, comment on blood film using manual methods. The conclusion of this study there is increase in total white blood cells with shift to left in neutrophil precursor in sickle cell patients with complications ,the most immature cells are band form, myelocytes and metamyelocytes, and there also lymphocytosis and neutrophilia which has been increases in response to infections.

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Segmentation of Leukemia Cells Using Clustering

International Journal of Synthetic Emotions ◽

10.4018/ijse.2019070103 ◽

2019 ◽

Vol 10 (2) ◽

pp. 39-48

Author(s):

Eman Mostafa ◽

Heba A. Tag El-Dien

Keyword(s):

Bone Marrow ◽

Cancer Cells ◽

Blood Cells ◽

White Blood Cells ◽

Leukemia Cells ◽

Microscopic Image ◽

Processing Stage ◽

Clustering Techniques ◽

Fuzzy C Means

Leukemia is a blood cancer which is defined as an irregular augment of undeveloped white blood cells called “blasts.” It develops in the bone marrow, which is responsible for blood cell generation including leukocytes and white blood cells. The early diagnosis of leukemia greatly helps in the treatment. Accordingly, researchers are interested in developing advanced and accurate automated techniques for localizing such abnormal blood cells. Subsequently, image segmentation becomes an important image processing stage for successful feature extraction and classification of leukemia in further stages. It aims to separate cancer cells by segmenting the microscopic image into background and cancer cells that are known as the region of interested (ROI). In this article, the cancer blood cells were segmented using two separated clustering techniques, namely the K-means and Fuzzy-c-means techniques. Then, the results of these techniques were compared to in terms of different segmentation metrics, such as the Dice, Jac, specificity, sensitivity, and accuracy. The results proved that the k-means provided better performance in leukemia blood cells segmentation as it achieved an accuracy of 99.8% compared to 99.6% with the fuzzy c-means.

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