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  • Open Access

    ARTICLE

    Semantic Segmentation of Lumbar Vertebrae Using Meijering U-Net (MU-Net) on Spine Magnetic Resonance Images

    Lakshmi S V V1, Shiloah Elizabeth Darmanayagam1,*, Sunil Retmin Raj Cyril2

    CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.1, pp. 733-757, 2025, DOI:10.32604/cmes.2024.056424 - 17 December 2024

    Abstract Lower back pain is one of the most common medical problems in the world and it is experienced by a huge percentage of people everywhere. Due to its ability to produce a detailed view of the soft tissues, including the spinal cord, nerves, intervertebral discs, and vertebrae, Magnetic Resonance Imaging is thought to be the most effective method for imaging the spine. The semantic segmentation of vertebrae plays a major role in the diagnostic process of lumbar diseases. It is difficult to semantically partition the vertebrae in Magnetic Resonance Images from the surrounding variety of… More >

  • Open Access

    ARTICLE

    Lung Nodule Detection Based on YOLOv3 Deep Learning with Limited Datasets

    Zhaohui Bu1, Xuejun Zhang2,3,*, Jianxiang Lu4, Huan Lao5, Chan Liang2, Xianfu Xu2, Yini Wei2, Hongjie Zeng2

    Molecular & Cellular Biomechanics, Vol.19, No.1, pp. 17-28, 2022, DOI:10.32604/mcb.2022.018318 - 12 January 2022

    Abstract The early symptom of lung tumor is always appeared as nodule on CT scans, among which 30% to 40% are malignant according to statistics studies. Therefore, early detection and classification of lung nodules are crucial to the treatment of lung cancer. With the increasing prevalence of lung cancer, large amount of CT images waiting for diagnosis are huge burdens to doctors who may missed or false detect abnormalities due to fatigue. Methods: In this study, we propose a novel lung nodule detection method based on YOLOv3 deep learning algorithm with only one preprocessing step is… More >

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