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  • Open Access


    Targeting brain tumors with innovative nanocarriers: bridging the gap through the blood-brain barrier


    Oncology Research, Vol.32, No.5, pp. 877-897, 2024, DOI:10.32604/or.2024.047278

    Abstract Background: Glioblastoma multiforme (GBM) is recognized as the most lethal and most highly invasive tumor. The high likelihood of treatment failure arises from the presence of the blood-brain barrier (BBB) and stem cells around GBM, which avert the entry of chemotherapeutic drugs into the tumor mass. Objective: Recently, several researchers have designed novel nanocarrier systems like liposomes, dendrimers, metallic nanoparticles, nanodiamonds, and nanorobot approaches, allowing drugs to infiltrate the BBB more efficiently, opening up innovative avenues to prevail over therapy problems and radiation therapy. Methods: Relevant literature for this manuscript has been collected from a comprehensive More > Graphic Abstract

    Targeting brain tumors with innovative nanocarriers: bridging the gap through the blood-brain barrier

  • Open Access


    Advancing Brain Tumor Analysis through Dynamic Hierarchical Attention for Improved Segmentation and Survival Prognosis

    S. Kannan1,*, S. Anusuya2

    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3835-3851, 2023, DOI:10.32604/cmc.2023.042465

    Abstract Gliomas, the most prevalent primary brain tumors, require accurate segmentation for diagnosis and risk assessment. In this paper, we develop a novel deep learning-based method, the Dynamic Hierarchical Attention for Improved Segmentation and Survival Prognosis (DHA-ISSP) model. The DHA-ISSP model combines a three-band 3D convolutional neural network (CNN) U-Net architecture with dynamic hierarchical attention mechanisms, enabling precise tumor segmentation and survival prediction. The DHA-ISSP model captures fine-grained details and contextual information by leveraging attention mechanisms at multiple levels, enhancing segmentation accuracy. By achieving remarkable results, our approach surpasses 369 competing teams in the 2020 Multimodal… More >

  • Open Access


    Classification of Brain Tumors Using Hybrid Feature Extraction Based on Modified Deep Learning Techniques

    Tawfeeq Shawly1, Ahmed Alsheikhy2,*

    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 425-443, 2023, DOI:10.32604/cmc.2023.040561

    Abstract According to the World Health Organization (WHO), Brain Tumors (BrT) have a high rate of mortality across the world. The mortality rate, however, decreases with early diagnosis. Brain images, Computed Tomography (CT) scans, Magnetic Resonance Imaging scans (MRIs), segmentation, analysis, and evaluation make up the critical tools and steps used to diagnose brain cancer in its early stages. For physicians, diagnosis can be challenging and time-consuming, especially for those with little expertise. As technology advances, Artificial Intelligence (AI) has been used in various domains as a diagnostic tool and offers promising outcomes. Deep-learning techniques are… More >

  • Open Access


    Novel Framework of Segmentation 3D MRI of Brain Tumors

    Ibrahim Mahmoud El-Henawy1, Mostafa Elbaz2, Zainab H. Ali3,*, Noha Sakr4

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3489-3502, 2023, DOI:10.32604/cmc.2023.033356

    Abstract Medical image segmentation is a crucial process for computer-aided diagnosis and surgery. Medical image segmentation refers to portioning the images into small, disjointed parts for simplifying the processes of analysis and examination. Rician and speckle noise are different types of noise in magnetic resonance imaging (MRI) that affect the accuracy of the segmentation process negatively. Therefore, image enhancement has a significant role in MRI segmentation. This paper proposes a novel framework that uses 3D MRI images from Kaggle and applies different diverse models to remove Rician and speckle noise using the best possible noise-free image.… More >

  • Open Access


    The Integrated Histopathologic and Molecular Approach to Adult-type Diffuse Astrocytomas: Status of the Art, Based on the 2021 WHO Classification of Central Nervous System Tumors

    Hiba Alzoubi1, Alameen Alsabbah2, Rosario Caltabiano3, Giuseppe Broggi3,*

    Oncologie, Vol.24, No.1, pp. 51-63, 2022, DOI:10.32604/oncologie.2022.020890

    Abstract The 2021 World Health Organization (WHO) Classification of Tumors of the Central Nervous System (CNS) improved our understanding of the brain neoplasm biology. In more details, differences between diffuse gliomas that primarily occur in adults and those that primarily occur in children have been identified by the terms “adult-type” and “pediatric-type” diffuse gliomas. More importantly, both diagnostic and grading criteria for adult-type diffuse astrocytomas have been modified, by adopting novel molecular markers: diffuse astrocytomas, IDH-mutant have been grouped into a single entity and graded as CNS WHO grades 2, 3, or 4, with the assignment of More >

  • Open Access


    AGWO-CNN Classification for Computer-Assisted Diagnosis of Brain Tumors

    T. Jeslin1,*, J. Arul Linsely2

    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 171-182, 2022, DOI:10.32604/cmc.2022.020255

    Abstract Brain cancer is the premier reason for cancer deaths all over the world. The diagnosis of brain cancer at an initial stage is mediocre, as the radiologist is ineffectual. Different experiments have been conducted and demonstrated clearly that the algorithms for nodule segmentation are unsuccessful. Therefore, the research has consolidated incremental clustering focused on superpixel segmentation as an appropriate optimization approach for the accurate segmentation of pulmonary nodules. The key aim of the research is to refine brain CT images to accurately distinguish tumors and the segmentation of small-scale anomalous nodules in the brain region.… More >

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