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  • Open Access


    DT-Net: Joint Dual-Input Transformer and CNN for Retinal Vessel Segmentation

    Wenran Jia1, Simin Ma1, Peng Geng1, Yan Sun2,*

    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3393-3411, 2023, DOI:10.32604/cmc.2023.040091

    Abstract Retinal vessel segmentation in fundus images plays an essential role in the screening, diagnosis, and treatment of many diseases. The acquired fundus images generally have the following problems: uneven illumination, high noise, and complex structure. It makes vessel segmentation very challenging. Previous methods of retinal vascular segmentation mainly use convolutional neural networks on U Network (U-Net) models, and they have many limitations and shortcomings, such as the loss of microvascular details at the end of the vessels. We address the limitations of convolution by introducing the transformer into retinal vessel segmentation. Therefore, we propose a hybrid method for retinal vessel… More >

  • Open Access


    Dual-Branch-UNet: A Dual-Branch Convolutional Neural Network for Medical Image Segmentation

    Muwei Jian1,2,#,*, Ronghua Wu1,#, Hongyu Chen1, Lanqi Fu3, Chengdong Yang1

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 705-716, 2023, DOI:10.32604/cmes.2023.027425

    Abstract In intelligent perception and diagnosis of medical equipment, the visual and morphological changes in retinal vessels are closely related to the severity of cardiovascular diseases (e.g., diabetes and hypertension). Intelligent auxiliary diagnosis of these diseases depends on the accuracy of the retinal vascular segmentation results. To address this challenge, we design a Dual-Branch-UNet framework, which comprises a Dual-Branch encoder structure for feature extraction based on the traditional U-Net model for medical image segmentation. To be more explicit, we utilize a novel parallel encoder made up of various convolutional modules to enhance the encoder portion of the original U-Net. Then, image… More >

  • Open Access


    SepFE: Separable Fusion Enhanced Network for Retinal Vessel Segmentation

    Yun Wu1, Ge Jiao1,2,*, Jiahao Liu1

    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.3, pp. 2465-2485, 2023, DOI:10.32604/cmes.2023.026189

    Abstract The accurate and automatic segmentation of retinal vessels from fundus images is critical for the early diagnosis and prevention of many eye diseases, such as diabetic retinopathy (DR). Existing retinal vessel segmentation approaches based on convolutional neural networks (CNNs) have achieved remarkable effectiveness. Here, we extend a retinal vessel segmentation model with low complexity and high performance based on U-Net, which is one of the most popular architectures. In view of the excellent work of depth-wise separable convolution, we introduce it to replace the standard convolutional layer. The complexity of the proposed model is reduced by decreasing the number of… More >

  • Open Access


    Modified Anam-Net Based Lightweight Deep Learning Model for Retinal Vessel Segmentation

    Syed Irtaza Haider1, Khursheed Aurangzeb2,*, Musaed Alhussein2

    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1501-1526, 2022, DOI:10.32604/cmc.2022.025479

    Abstract The accurate segmentation of retinal vessels is a challenging task due to the presence of various pathologies as well as the low-contrast of thin vessels and non-uniform illumination. In recent years, encoder-decoder networks have achieved outstanding performance in retinal vessel segmentation at the cost of high computational complexity. To address the aforementioned challenges and to reduce the computational complexity, we propose a lightweight convolutional neural network (CNN)-based encoder-decoder deep learning model for accurate retinal vessels segmentation. The proposed deep learning model consists of encoder-decoder architecture along with bottleneck layers that consist of depth-wise squeezing, followed by full-convolution, and finally depth-wise… More >

  • Open Access


    MIA-UNet: Multi-Scale Iterative Aggregation U-Network for Retinal Vessel Segmentation

    Linfang Yu, Zhen Qin*, Yi Ding, Zhiguang Qin

    CMES-Computer Modeling in Engineering & Sciences, Vol.129, No.2, pp. 805-828, 2021, DOI:10.32604/cmes.2021.017332

    Abstract As an important part of the new generation of information technology, the Internet of Things (IoT) has been widely concerned and regarded as an enabling technology of the next generation of health care system. The fundus photography equipment is connected to the cloud platform through the IoT, so as to realize the real-time uploading of fundus images and the rapid issuance of diagnostic suggestions by artificial intelligence. At the same time, important security and privacy issues have emerged. The data uploaded to the cloud platform involves more personal attributes, health status and medical application data of patients. Once leaked, abused… More >

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