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  • Open Access


    DCFNet: An Effective Dual-Branch Cross-Attention Fusion Network for Medical Image Segmentation

    Chengzhang Zhu1,2, Renmao Zhang1, Yalong Xiao1,2,*, Beiji Zou1, Xian Chai1, Zhangzheng Yang1, Rong Hu3, Xuanchu Duan4

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 1103-1128, 2024, DOI:10.32604/cmes.2024.048453

    Abstract Automatic segmentation of medical images provides a reliable scientific basis for disease diagnosis and analysis. Notably, most existing methods that combine the strengths of convolutional neural networks (CNNs) and Transformers have made significant progress. However, there are some limitations in the current integration of CNN and Transformer technology in two key aspects. Firstly, most methods either overlook or fail to fully incorporate the complementary nature between local and global features. Secondly, the significance of integrating the multi-scale encoder features from the dual-branch network to enhance the decoding features is often disregarded in methods that combine… More >

  • Open Access


    Intelligent Fault Diagnosis Method of Rolling Bearings Based on Transfer Residual Swin Transformer with Shifted Windows

    Haomiao Wang1, Jinxi Wang2, Qingmei Sui2,*, Faye Zhang2, Yibin Li1, Mingshun Jiang2, Phanasindh Paitekul3

    Structural Durability & Health Monitoring, Vol.18, No.2, pp. 91-110, 2024, DOI:10.32604/sdhm.2023.041522

    Abstract Due to their robust learning and expression ability for complex features, the deep learning (DL) model plays a vital role in bearing fault diagnosis. However, since there are fewer labeled samples in fault diagnosis, the depth of DL models in fault diagnosis is generally shallower than that of DL models in other fields, which limits the diagnostic performance. To solve this problem, a novel transfer residual Swin Transformer (RST) is proposed for rolling bearings in this paper. RST has 24 residual self-attention layers, which use the hierarchical design and the shifted window-based residual self-attention. Combined More >

  • Open Access


    SwinVid: Enhancing Video Object Detection Using Swin Transformer

    Abdelrahman Maharek1,2,*, Amr Abozeid2,3, Rasha Orban1, Kamal ElDahshan2

    Computer Systems Science and Engineering, Vol.48, No.2, pp. 305-320, 2024, DOI:10.32604/csse.2024.039436

    Abstract What causes object detection in video to be less accurate than it is in still images? Because some video frames have degraded in appearance from fast movement, out-of-focus camera shots, and changes in posture. These reasons have made video object detection (VID) a growing area of research in recent years. Video object detection can be used for various healthcare applications, such as detecting and tracking tumors in medical imaging, monitoring the movement of patients in hospitals and long-term care facilities, and analyzing videos of surgeries to improve technique and training. Additionally, it can be used… More >

  • Open Access


    Liver Tumor Segmentation Based on Multi-Scale and Self-Attention Mechanism

    Fufang Li, Manlin Luo*, Ming Hu, Guobin Wang, Yan Chen

    Computer Systems Science and Engineering, Vol.47, No.3, pp. 2835-2850, 2023, DOI:10.32604/csse.2023.039765

    Abstract Liver cancer has the second highest incidence rate among all types of malignant tumors, and currently, its diagnosis heavily depends on doctors’ manual labeling of CT scan images, a process that is time-consuming and susceptible to subjective errors. To address the aforementioned issues, we propose an automatic segmentation model for liver and tumors called Res2Swin Unet, which is based on the Unet architecture. The model combines Attention-Res2 and Swin Transformer modules for liver and tumor segmentation, respectively. Attention-Res2 merges multiple feature map parts with an Attention gate via skip connections, while Swin Transformer captures long-range More >

  • Open Access


    Clinical Knowledge-Based Hybrid Swin Transformer for Brain Tumor Segmentation

    Xiaoliang Lei1, Xiaosheng Yu2,*, Hao Wu3, Chengdong Wu2,*, Jingsi Zhang2

    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3797-3811, 2023, DOI:10.32604/cmc.2023.042069

    Abstract Accurate tumor segmentation from brain tissues in Magnetic Resonance Imaging (MRI) imaging is crucial in the pre-surgical planning of brain tumor malignancy. MRI images’ heterogeneous intensity and fuzzy boundaries make brain tumor segmentation challenging. Furthermore, recent studies have yet to fully employ MRI sequences’ considerable and supplementary information, which offers critical a priori knowledge. This paper proposes a clinical knowledge-based hybrid Swin Transformer multimodal brain tumor segmentation algorithm based on how experts identify malignancies from MRI images. During the encoder phase, a dual backbone network with a Swin Transformer backbone to capture long dependencies from… More >

  • Open Access


    A Novel-based Swin Transfer Based Diagnosis of COVID-19 Patients

    Yassir Edrees Almalki1, Maryam Zaffar2,*, Muhammad Irfan3, Mohammad Ali Abbas2, Maida Khalid2, K.S. Quraishi4, Tariq Ali5, Fahad Alshehri6, Sharifa Khalid Alduraibi6, Abdullah A. Asiri7, Mohammad Abd Alkhalik Basha8, Alaa Alduraibi6, M.K. Saeed7, Saifur Rahman3

    Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 163-180, 2023, DOI:10.32604/iasc.2023.025580

    Abstract The numbers of cases and deaths due to the COVID-19 virus have increased daily all around the world. Chest X-ray is considered very useful and less time-consuming for monitoring COVID disease. No doubt, X-ray is considered as a quick screening method, but due to variations in features of images which are of X-rays category with Corona confirmed cases, the domain expert is needed. To address this issue, we proposed to utilize deep learning approaches. In this study, the dataset of COVID-19, lung opacity, viral pneumonia, and lastly healthy patients’ images of category X-rays are utilized… More >

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