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  • Open Access


    Histogram-Based Decision Support System for Extraction and Classification of Leukemia in Blood Smear Images

    Neenavath Veeraiah1,*, Youseef Alotaibi2, Ahmad F. Subahi3

    Computer Systems Science and Engineering, Vol.46, No.2, pp. 1879-1900, 2023, DOI:10.32604/csse.2023.034658

    Abstract An abnormality that develops in white blood cells is called leukemia. The diagnosis of leukemia is made possible by microscopic investigation of the smear in the periphery. Prior training is necessary to complete the morphological examination of the blood smear for leukemia diagnosis. This paper proposes a Histogram Threshold Segmentation Classifier (HTsC) for a decision support system. The proposed HTsC is evaluated based on the color and brightness variation in the dataset of blood smear images. Arithmetic operations are used to crop the nucleus based on automated approximation. White Blood Cell (WBC) segmentation is calculated using the active contour model… More >

  • Open Access


    A Novel Meta-Heuristic Optimization Algorithm in White Blood Cells Classification

    Khaled A. Fathy, Humam K. Yaseen*, Mohammad T. Abou-Kreisha, Kamal A. ElDahshan

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1527-1545, 2023, DOI:10.32604/cmc.2023.036322

    Abstract Some human diseases are recognized through of each type of White Blood Cell (WBC) count, so detecting and classifying each type is important for human healthcare. The main aim of this paper is to propose a computer-aided WBCs utility analysis tool designed, developed, and evaluated to classify WBCs into five types namely neutrophils, eosinophils, lymphocytes, monocytes, and basophils. Using a computer-artificial model reduces resource and time consumption. Various pre-trained deep learning models have been used to extract features, including AlexNet, Visual Geometry Group (VGG), Residual Network (ResNet), which belong to different taxonomy types of deep learning architectures. Also, Binary Border… More >

  • Open Access


    Automated Artificial Intelligence Empowered White Blood Cells Classification Model

    Mohammad Yamin1, Abdullah M. Basahel1, Mona Abusurrah2, Sulafah M Basahel3, Sachi Nandan Mohanty4, E. Laxmi Lydia5,*

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 409-425, 2023, DOI:10.32604/cmc.2023.032432

    Abstract White blood cells (WBC) or leukocytes are a vital component of the blood which forms the immune system, which is accountable to fight foreign elements. The WBC images can be exposed to different data analysis approaches which categorize different kinds of WBC. Conventionally, laboratory tests are carried out to determine the kind of WBC which is erroneous and time consuming. Recently, deep learning (DL) models can be employed for automated investigation of WBC images in short duration. Therefore, this paper introduces an Aquila Optimizer with Transfer Learning based Automated White Blood Cells Classification (AOTL-WBCC) technique. The presented AOTL-WBCC model executes… More >

  • Open Access


    Automated White Blood Cell Disease Recognition Using Lightweight Deep Learning

    Abdullah Alqahtani1, Shtwai Alsubai1, Mohemmed Sha1,*, Muhammad Attique Khan2, Majed Alhaisoni3, Syed Rameez Naqvi2

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 107-123, 2023, DOI:10.32604/csse.2023.030727

    Abstract White blood cells (WBC) are immune system cells, which is why they are also known as immune cells. They protect the human body from a variety of dangerous diseases and outside invaders. The majority of WBCs come from red bone marrow, although some come from other important organs in the body. Because manual diagnosis of blood disorders is difficult, it is necessary to design a computerized technique. Researchers have introduced various automated strategies in recent years, but they still face several obstacles, such as imbalanced datasets, incorrect feature selection, and incorrect deep model selection. We proposed an automated deep learning… More >

  • Open Access


    Oxidative stress indicators in human and bottlenose dolphin leukocytes in response to a pro-inflammatory challenge


    BIOCELL, Vol.45, No.6, pp. 1621-1630, 2021, DOI:10.32604/biocell.2021.016302

    Abstract Marine mammals undergo cycles of tissue ischemia and reperfusion during the dive response. Reperfusion injury can result in oxidative tissue damage and the activation of a pro-inflammatory immune response. The risk of oxidative damage is reduced by antioxidants. Our hypothesis is that the reported higher antioxidant defenses within marine mammal tissues provide additional protection in situations that produce oxidative stress, like inflammation, in comparison to terrestrial mammal tissues. Leukocytes were isolated from the whole blood of Pacific bottlenose dolphins (Tursiops truncatus gilli) and humans (Homo sapiens) and were exposed to lipopolysaccharides (LPS, 10 µg/mL) in vitro to simulate a pro-inflammatory… More >

  • Open Access


    Diagnosis of Leukemia Disease Based on Enhanced Virtual Neural Network

    K. Muthumayil1, S. Manikandan2, S. Srinivasan3, José Escorcia-Gutierrez4,*, Margarita Gamarra5, Romany F. Mansour6

    CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2031-2044, 2021, DOI:10.32604/cmc.2021.017116

    Abstract White Blood Cell (WBC) cancer or leukemia is one of the serious cancers that threaten the existence of human beings. In spite of its prevalence and serious consequences, it is mostly diagnosed through manual practices. The risks of inappropriate, sub-standard and wrong or biased diagnosis are high in manual methods. So, there is a need exists for automatic diagnosis and classification method that can replace the manual process. Leukemia is mainly classified into acute and chronic types. The current research work proposed a computer-based application to classify the disease. In the feature extraction stage, we use excellent physical properties to… More >

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