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Acute lymphoblastic leukemia (ALL) is a disease that is detected by the presence of lymphoblast cell. Basically, lymphoblast cell is the abnormal cell of lymphocyte which is one of the White Blood Cell (WBC) types. Early prevention is suggested as this disease can be fatal and caused death. Traditionally, ALL is detected by using manual analysis wh...
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... method is applied once for each segmented image. The result of morphological filter application is as shown in Figure 8. However, getting rid of the unwanted region is not an easy task. ...
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... When ALL happens that WBCs fully mature, this type of leukemia is more common in children. Lack of treatment of this disease causes many lymphoblasts are produced in the body and can lead to death [14,51,59]. ALL occurs more often in B than in T cells. ...
... In the preprocessing step of some works, the RGB image was changed to L*a*b color space as the input image of the network [59] [17] [22] [32] [2].L*a*b* is denoted by lightness (L*) and two color components are represented by (a*) and (b*). In [58] [28] transformed RGB to HSV color space as the input image format. ...
Leukemia (blood cancer) is an unusual spread of White Blood Cells or Leukocytes (WBCs) in the bone marrow and blood. Pathologists can diagnose leukemia by looking at a person's blood sample under a microscope. They identify and categorize leukemia by counting various blood cells and morphological features. This technique is time-consuming for the prediction of leukemia. The pathologist's professional skills and experiences may be affecting this procedure, too. In computer vision, traditional machine learning and deep learning techniques are practical roadmaps that increase the accuracy and speed in diagnosing and classifying medical images such as microscopic blood cells. This paper provides a comprehensive analysis of the detection and classification of acute leukemia and WBCs in the microscopic blood cells. First, we have divided the previous works into six categories based on the output of the models. Then, we describe various steps of detection and classification of acute leukemia and WBCs, including Data Augmentation, Preprocessing, Segmentation, Feature Extraction, Feature Selection (Reduction), Classification, and focus on classification step in the methods. Finally, we divide automated detection and classification of acute leukemia and WBCs into three categories, including traditional, Deep Neural Network (DNN), and mixture (traditional and DNN) methods based on the type of classifier in the classification step and analyze them. The results of this study show that in the diagnosis and classification of acute leukemia and WBCs, the Support Vector Machine (SVM) classifier in traditional machine learning models and Convolutional Neural Network (CNN) classifier in deep learning models have widely employed. The performance metrics of the models that use these classifiers compared to the others model are higher.
... When ALL happens that WBCs fully mature, this type of leukemia is more common in children. Lack of treatment of this disease causes many lymphoblasts are produced in the body and can lead to death [14,51,59]. ALL occurs more often in B than in T cells. ...
... In the preprocessing step of some works, the RGB image was changed to L*a*b color space as the input image of the network [59] [17] [22] [32] [2].L*a*b* is denoted by lightness (L*) and two color components are represented by (a*) and (b*). In [58] [28] transformed RGB to HSV color space as the input image format. ...
Leukemia (blood cancer) is an unusual spread of White Blood Cells or Leukocytes (WBCs) in the bone marrow and blood. Pathologists can diagnose leukemia by looking at a person’s blood sample under a microscope. They identify and categorize leukemia by counting various blood cells and morphological features. This technique is time-consuming for the prediction of leukemia. The pathologist’s professional skills and experiences may be affecting this procedure, too. In computer vision, traditional machine learning and deep learning techniques are practical roadmaps that increase the accuracy and speed in diagnosing and classifying medical images such as microscopic blood cells. This paper provides a comprehensive analysis of the detection and classification of acute leukemia and WBCs in the microscopic blood cells. First, we have divided the previous works into six categories based on the output of the models. Then, we describe various steps of detection and classification of acute leukemia and WBCs, including Data Augmentation, Preprocessing, Segmentation, Feature Extraction, Feature Selection (Reduction), Classification, and focus on classification step in the methods. Finally, we divide automated detection and classification of acute leukemia and WBCs into three categories, including traditional, Deep Neural Network (DNN), and mixture (traditional and DNN) methods based on the type of classifier in the classification step and analyze them. The results of this study show that in the diagnosis and classification of acute leukemia and WBCs, the Support Vector Machine (SVM) classifier in traditional machine learning models and Convolutional Neural Network (CNN) classifier in deep learning models have widely employed. The performance metrics of the models that use these classifiers compared to the others model are higher. We propose providing models in detecting and classify acute leukemia and WBCs that use a combination of SVM and CNN classifiers in their classification step to achieve optimum performance metrics.
... In grown-ups, the marrow that has the maximal activity is in the spine bones (also referred to as the vertebrae), breast bone, bones of the pelvis, bones of the shoulders, rib bones, and the skull. Undeveloped blood cells in the marrow are known as the stem cells, residing in the bloodstream as well, but in smaller amounts, and are called peripheral blood stem cells, which could be abbreviated to (PBSCs) [12]. ...
The heterogeneous texture classifications with the complexity of structures provide variety of possibilities in image processing, as an example of the multifractal analysis features. The task of texture analysis is a highly significant field of study in the field of machine vision. Most of the real-life surfaces exhibit textures and an efficiently modelled vision system must have the ability to deal with this variety of surfaces. A considerable number of surfaces maintain a self-similarity quality as well as statistical roughness at different scales. Fractals could provide a great deal of advantages; also, they are popular in the process of modelling these properties in the tasks related to the field of image processing. With two distinct methods, this paper presents classification of texture using random box counting and binarization methods calculate the estimation measures of the fractal dimension BCM. There methods are the banalization and random selecting boxes. The classification of the white blood cells is presented in this paper based on the texture if it is normal or abnormal with the use of a number of various methods.
... b. Support vector machine (SVM) model Another supervised learning algorithm is selected, which is known to be strong algorithm used for classification and regression used in different domain, such as Healthcare [26], intrusion detection system [27], lymphoblast classification [28] and driving simulators [29]. It also helps to detect outliers using a built-in function. ...
