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  • Open Access

    ARTICLE

    Early detection of breast cancer in mammograms using the lightweight modification of efficientNet B3

    Nabilah Ruza1, Saiful Izzuan Hussain2, Siti Kamariah Che Mohamed3, Mohd Hafiz Arzmi4,5

    Revista Internacional de Métodos Numéricos para Cálculo y Diseño en Ingeniería, Vol.39, No.3, pp. 1-7, 2023, DOI:10.23967/j.rimni.2023.08.002 - 01 September 2023

    Abstract Breast cancer is one of the leading causes of death in women worldwide and early detection is critical to improving survival rates. In this study, we present a modified deep learning method for automatic feature detection for breast mass classification on mammograms. We propose to use EfficientNet, a Convolutional Neural Network (CNN) architecture that requires minimal parameters. The main advantage of EfficientNet is the small number of parameters, which allows efficient and accurate classification of mammogram images. Our experiments show that EfficientNet, with an overall accuracy of 86.5 percent, has the potential to be the More >

  • Open Access

    ARTICLE

    Hybrid Active Contour Mammographic Mass Segmentation and Classification

    K. Yuvaraj*, U. S. Ragupathy

    Computer Systems Science and Engineering, Vol.40, No.3, pp. 823-834, 2022, DOI:10.32604/csse.2022.018837 - 24 September 2021

    Abstract This research implements a novel segmentation of mammographic mass. Three methods are proposed, namely, segmentation of mass based on iterative active contour, automatic region growing, and fully automatic mask selection-based active contour techniques. In the first method, iterative threshold is performed for manual cropped preprocessed image, and active contour is applied thereafter. To overcome manual cropping in the second method, an automatic seed selection followed by region growing is performed. Given that the result is only a few images owing to over segmentation, the third method uses a fully automatic active contour. Results of the… More >

  • Open Access

    ARTICLE

    Kinematic Analysis and Rock Mass Classifications for Rock Slope Failure at USAID Highways

    Ibnu Rusydy1,3,*, Nafisah Al-Huda1,2, M. Fahmi4, Naufal Effendi4

    Structural Durability & Health Monitoring, Vol.13, No.4, pp. 379-398, 2019, DOI:10.32604/sdhm.2019.08192

    Abstract Rock slope kinematic analysis and rock mass classifications has been conducted at the 17th km to 26th km of USAID (United States Agency for International Development) highway in Indonesia. This research aimed to examine the type of rock slope failures and the quality of rock mass as well. The scan-line method was performed in six slopes by using a geological compass to determine rock mass structure on the rock slope, and the condition of joints such as persistence, aperture, roughness, infilling material, weathering and groundwater conditions. Slope kinematic analysis was performed employing a stereographic projection. The… More >

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