Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (7)
  • Open Access


    A Novel Approach to Breast Tumor Detection: Enhanced Speckle Reduction and Hybrid Classification in Ultrasound Imaging

    K. Umapathi1,*, S. Shobana1, Anand Nayyar2, Judith Justin3, R. Vanithamani3, Miguel Villagómez Galindo4, Mushtaq Ahmad Ansari5, Hitesh Panchal6,*

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 1875-1901, 2024, DOI:10.32604/cmc.2024.047961

    Abstract Breast cancer detection heavily relies on medical imaging, particularly ultrasound, for early diagnosis and effective treatment. This research addresses the challenges associated with computer-aided diagnosis (CAD) of breast cancer from ultrasound images. The primary challenge is accurately distinguishing between malignant and benign tumors, complicated by factors such as speckle noise, variable image quality, and the need for precise segmentation and classification. The main objective of the research paper is to develop an advanced methodology for breast ultrasound image classification, focusing on speckle noise reduction, precise segmentation, feature extraction, and machine learning-based classification. A unique approach… More >

  • Open Access


    Proposed Framework for Detection of Breast Tumors

    Mostafa Elbaz1,2,*, Haitham Elwahsh1, Ibrahim Mahmoud El-Henawy2

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2927-2944, 2023, DOI:10.32604/cmc.2023.033111

    Abstract Computer vision is one of the significant trends in computer science. It plays as a vital role in many applications, especially in the medical field. Early detection and segmentation of different tumors is a big challenge in the medical world. The proposed framework uses ultrasound images from Kaggle, applying five diverse models to denoise the images, using the best possible noise-free image as input to the U-Net model for segmentation of the tumor, and then using the Convolution Neural Network (CNN) model to classify whether the tumor is benign, malignant, or normal. The main challenge… More >

  • Open Access


    Pre-Trained Deep Neural Network-Based Computer-Aided Breast Tumor Diagnosis Using ROI Structures

    Venkata Sunil Srikanth*, S. Krithiga

    Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 63-78, 2023, DOI:10.32604/iasc.2023.023474

    Abstract Deep neural network (DNN) based computer-aided breast tumor diagnosis (CABTD) method plays a vital role in the early detection and diagnosis of breast tumors. However, a Brightness mode (B-mode) ultrasound image derives training feature samples that make closer isolation toward the infection part. Hence, it is expensive due to a meta-heuristic search of features occupying the global region of interest (ROI) structures of input images. Thus, it may lead to the high computational complexity of the pre-trained DNN-based CABTD method. This paper proposes a novel ensemble pre-trained DNN-based CABTD method using global- and local-ROI-structures of… More >

  • Open Access


    Breast Tumor Computer-Aided Detection System Based on Magnetic Resonance Imaging Using Convolutional Neural Network

    Jing Lu1, Yan Wu2,#, Mingyan Hu1, Yao Xiong1, Yapeng Zhou1, Ziliang Zhao1, Liutong Shang1,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.1, pp. 365-377, 2022, DOI:10.32604/cmes.2021.017897

    Abstract Background: The main cause of breast cancer is the deterioration of malignant tumor cells in breast tissue. Early diagnosis of tumors has become the most effective way to prevent breast cancer. Method: For distinguishing between tumor and non-tumor in MRI, a new type of computer-aided detection CAD system for breast tumors is designed in this paper. The CAD system was constructed using three networks, namely, the VGG16, Inception V3, and ResNet50. Then, the influence of the convolutional neural network second migration on the experimental results was further explored in the VGG16 system. Result: CAD system built based… More >

  • Open Access


    Residual U-Network for Breast Tumor Segmentation from Magnetic Resonance Images

    Ishu Anand1, Himani Negi1, Deepika Kumar1, Mamta Mittal2, Tai-hoon Kim3,*, Sudipta Roy4

    CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 3107-3127, 2021, DOI:10.32604/cmc.2021.014229

    Abstract Breast cancer positions as the most well-known threat and the main source of malignant growth-related morbidity and mortality throughout the world. It is apical of all new cancer incidences analyzed among females. Two features substantially influence the classification accuracy of malignancy and benignity in automated cancer diagnostics. These are the precision of tumor segmentation and appropriateness of extracted attributes required for the diagnosis. In this research, the authors have proposed a ResU-Net (Residual U-Network) model for breast tumor segmentation. The proposed methodology renders augmented, and precise identification of tumor regions and produces accurate breast tumor… More >

  • Open Access


    Suspension state promotes extravasation of breast tumor cells by increasing integrin β1 expression

    Bingbing ZHANG1, 2, Ying ZHANG1, 2, Xiaomei ZHANG1, 2, Yonggang LV1, 2

    BIOCELL, Vol.42, No.1, pp. 17-24, 2018, DOI:10.32604/biocell.2018.06115

    Abstract Mechanical microenvironment can strongly affect the metastatic efficiency of circulating tumor cells. However, the effect of suspension state on their extravasation and the mechanisms involved are still unclear. To explore the influence of suspension state on extravasation (including adhesion, spreading and transendothelial migration) of breast tumor cells and its relevant molecular mechanism, MDA-MB-231 cells were cultured on poly (2-hydroxyethyl methacrylate) coated 6-well plates to minic the suspension state. Suspension state promoted adhesion, spreading and transendothelial migration of MDA-MB-231 cells to EAhy926 endothelial cells (ECs) monolayer under both the static condition and 0.5 dyne/cm2 flow shear stress More >

  • Open Access


    Image Recognition of Breast Tumor Proliferation Level Based on Convolution Neural Network

    Junhao Yang1, Chunxiao Chen1,*, Qingyang Zang1, Jianfei Li1

    Molecular & Cellular Biomechanics, Vol.15, No.4, pp. 203-214, 2018, DOI:10.32604/mcb.2018.03824

    Abstract Pathological slide is increasingly applied in the diagnosis of breast tumors despite the issues of large amount of data, slow viewing and high subjectivity. To overcome these problems, a micrograph recognition method based on convolutional neural network is proposed for pathological slide of breast tumor. Combined with multi-channel threshold and watershed segmentation, a sample database including single cell, adhesive cell and invalid cell was established. Then, the convolution neural network with six layers is constructed, which has ability to classify the stained breast tumor cells with accuracy of more than 90%, and evaluate the proliferation More >

Displaying 1-10 on page 1 of 7. Per Page