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  • Open Access

    ARTICLE

    Bone marrow microRNA-34a is a good indicator for response to treatment in acute myeloid leukemia

    MONA S. ABDELLATEIF1,*, NAGLAA M. HASSAN2, MAHMOUD M. KAMEL2, YOMNA M. EL-MELIGUI2

    Oncology Research, Vol.32, No.3, pp. 577-584, 2024, DOI:10.32604/or.2023.043026

    Abstract Background: microRNA-34a (miR-34a) had been reported to have a diagnostic role in acute myeloid leukemia (AML). However, its value in the bone marrow (BM) of AML patients, in addition to its role in response to therapy is still unclear. The current study was designed to assess the diagnostic, prognostic, and predictive significance of miR-34a in the BM of AML patients. Methods: The miR-34a was assessed in BM aspirate of 82 AML patients in relation to 12 normal control subjects using qRT-PCR. The data were assessed for correlation with the relevant clinical criteria, response to therapy, disease-free survival (DFS), and overall… More >

  • Open Access

    ARTICLE

    A New Method for Diagnosis of Leukemia Utilizing a Hybrid DL-ML Approach for Binary and Multi-Class Classification on a Limited-Sized Database

    Nilkanth Mukund Deshpande1,2, Shilpa Gite3,4,*, Biswajeet Pradhan5,6, Abdullah Alamri7, Chang-Wook Lee8,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 593-631, 2024, DOI:10.32604/cmes.2023.030704

    Abstract Infection of leukemia in humans causes many complications in its later stages. It impairs bone marrow’s ability to produce blood. Morphological diagnosis of human blood cells is a well-known and well-proven technique for diagnosis in this case. The binary classification is employed to distinguish between normal and leukemia-infected cells. In addition, various subtypes of leukemia require different treatments. These sub-classes must also be detected to obtain an accurate diagnosis of the type of leukemia. This entails using multi-class classification to determine the leukemia subtype. This is usually done using a microscopic examination of these blood cells. Due to the requirement… More > Graphic Abstract

    A New Method for Diagnosis of Leukemia Utilizing a Hybrid DL-ML Approach for Binary and Multi-Class Classification on a Limited-Sized Database

  • Open Access

    REVIEW

    A Survey on Acute Leukemia Expression Data Classification Using Ensembles

    Abdel Nasser H. Zaied1, Ehab Rushdy2, Mona Gamal3,*

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 1349-1364, 2023, DOI:10.32604/csse.2023.033596

    Abstract Acute leukemia is an aggressive disease that has high mortality rates worldwide. The error rate can be as high as 40% when classifying acute leukemia into its subtypes. So, there is an urgent need to support hematologists during the classification process. More than two decades ago, researchers used microarray gene expression data to classify cancer and adopted acute leukemia as a test case. The high classification accuracy they achieved confirmed that it is possible to classify cancer subtypes using microarray gene expression data. Ensemble machine learning is an effective method that combines individual classifiers to classify new samples. Ensemble classifiers… More >

  • Open Access

    ARTICLE

    Automated Leukemia Screening and Sub-types Classification Using Deep Learning

    Chaudhary Hassan Abbas Gondal1,*, Muhammad Irfan2, Sarmad Shafique3, Muhammad Salman Bashir4, Mansoor Ahmed1, Osama M.Alshehri5, Hassan H. Almasoudi5, Samar M. Alqhtani6, Mohammed M. Jalal7, Malik A. Altayar7, Khalaf F. Alsharif8

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 3541-3558, 2023, DOI:10.32604/csse.2023.036476

    Abstract Leukemia is a kind of blood cancer that damages the cells in the blood and bone marrow of the human body. It produces cancerous blood cells that disturb the human’s immune system and significantly affect bone marrow’s production ability to effectively create different types of blood cells like red blood cells (RBCs) and white blood cells (WBC), and platelets. Leukemia can be diagnosed manually by taking a complete blood count test of the patient’s blood, from which medical professionals can investigate the signs of leukemia cells. Furthermore, two other methods, microscopic inspection of blood smears and bone marrow aspiration, are… More >

  • Open Access

    ARTICLE

    MayGAN: Mayfly Optimization with Generative Adversarial Network-Based Deep Learning Method to Classify Leukemia Form Blood Smear Images

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

    Computer Systems Science and Engineering, Vol.46, No.2, pp. 2039-2058, 2023, DOI:10.32604/csse.2023.036985

    Abstract Leukemia, often called blood cancer, is a disease that primarily affects white blood cells (WBCs), which harms a person’s tissues and plasma. This condition may be fatal when if it is not diagnosed and recognized at an early stage. The physical technique and lab procedures for Leukaemia identification are considered time-consuming. It is crucial to use a quick and unexpected way to identify different forms of Leukaemia. Timely screening of the morphologies of immature cells is essential for reducing the severity of the disease and reducing the number of people who require treatment. Various deep-learning (DL) model-based segmentation and categorization… More >

  • Open Access

    ARTICLE

    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

    ARTICLE

    Identification of lncRNAs associated with the progression of acute lymphoblastic leukemia using a competing endogenous RNAs network

    SHAHRAM NEKOEIAN1, TAHEREH ROSTAMI2, AMIR NOROUZY3, SAFIN HUSSEIN1,4, GHOLAMREZA TAVOOSIDANA1, BAHRAM CHAHARDOULI2, SHAHRBANO ROSTAMI2,*, YAZDAN ASGARI5, ZAHRA AZIZI1,*

    Oncology Research, Vol.30, No.6, pp. 259-268, 2022, DOI:10.32604/or.2022.027904

    Abstract Acute lymphoblastic leukemia (ALL) is a malignancy of bone marrow lymphoid precursors. Despite effective treatments, the causes of its progression or recurrence are still unknown. Finding prognostic biomarkers is needed for early diagnosis and more effective treatment. This study was performed to identify long non-coding RNAs (lncRNAs) involved in ALL progression by constructing a competitive endogenous RNA (ceRNA) network. These lncRNAs may serve as potential new biomarkers in the development of ALL. The GSE67684 dataset identified changes in lncRNAs and mRNAs involved in ALL progression. Data from this study were re-analyzed, and probes related to lncRNAs were retrieved. Targetscan, miRTarBase,… More >

  • Open Access

    ARTICLE

    A model based on eight iron metabolism-related genes accurately predicts acute myeloid leukemia prognosis

    ZHANSHU LIU1, XI HUANG2,*

    BIOCELL, Vol.47, No.3, pp. 593-605, 2023, DOI:10.32604/biocell.2023.024148

    Abstract Purpose: Iron metabolism maintains the balance between iron absorption and excretion. Abnormal iron metabolism can cause numerous diseases, including tumor. This study determined the iron metabolism-related genes (IMRGs) signature that can predict the prognosis of acute myeloid leukemia (AML). The roles of these genes in the immune microenvironment were also explored. Methods: A total of 514 IMRGs were downloaded from the Molecular Characteristics Database (MSigDB). IMRGs related to AML prognosis were identified using Cox regression and LASSO analyses and were used to construct the risk score model. AML patients were stratified into high-risk groups (cluster 1) and low-risk groups (cluster… More >

  • Open Access

    ARTICLE

    A Construction of Object Detection Model for Acute Myeloid Leukemia

    K. Venkatesh1,*, S. Pasupathy1, S. P. Raja2

    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 543-560, 2023, DOI:10.32604/iasc.2023.030701

    Abstract The evolution of bone marrow morphology is necessary in Acute Myeloid Leukemia (AML) prediction. It takes an enormous number of times to analyze with the standardization and inter-observer variability. Here, we proposed a novel AML detection model using a Deep Convolutional Neural Network (D-CNN). The proposed Faster R-CNN (Faster Region-Based CNN) models are trained with Morphological Dataset. The proposed Faster R-CNN model is trained using the augmented dataset. For overcoming the Imbalanced Data problem, data augmentation techniques are imposed. The Faster R-CNN performance was compared with existing transfer learning techniques. The results show that the Faster R-CNN performance was significant… More >

  • Open Access

    ARTICLE

    Latent Space Representational Learning of Deep Features for Acute Lymphoblastic Leukemia Diagnosis

    Ghada Emam Atteia*

    Computer Systems Science and Engineering, Vol.45, No.1, pp. 361-376, 2023, DOI:10.32604/csse.2023.029597

    Abstract Acute Lymphoblastic Leukemia (ALL) is a fatal malignancy that is featured by the abnormal increase of immature lymphocytes in blood or bone marrow. Early prognosis of ALL is indispensable for the effectual remediation of this disease. Initial screening of ALL is conducted through manual examination of stained blood smear microscopic images, a process which is time-consuming and prone to errors. Therefore, many deep learning-based computer-aided diagnosis (CAD) systems have been established to automatically diagnose ALL. This paper proposes a novel hybrid deep learning system for ALL diagnosis in blood smear images. The introduced system integrates the proficiency of autoencoder networks… More >

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