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  • Open Access


    Semi-Supervised Medical Image Segmentation Based on Generative Adversarial Network

    Yun Tan1,2, Weizhao Wu2, Ling Tan3, Haikuo Peng2, Jiaohua Qin2,*

    Journal of New Media, Vol.4, No.3, pp. 155-164, 2022, DOI:10.32604/jnm.2022.031113

    Abstract At present, segmentation for medical image is mainly based on fully supervised model training, which consumes a lot of time and labor for dataset labeling. To address this issue, we propose a semi-supervised medical image segmentation model based on a generative adversarial network framework for automated segmentation of arteries. The network is mainly composed of two parts: a segmentation network for medical image segmentation and a discriminant network for evaluating segmentation results. In the initial stage of network training, a fully supervised training method is adopted to make the segmentation network and the discrimination network have certain segmentation and discrimination… More >

  • Open Access


    Brain Tumor Auto-Segmentation on Multimodal Imaging Modalities Using Deep Neural Network

    Elias Hossain1, Md. Shazzad Hossain2, Md. Selim Hossain3, Sabila Al Jannat4, Moontahina Huda5, Sameer Alsharif6, Osama S. Faragallah7, Mahmoud M. A. Eid8, Ahmed Nabih Zaki Rashed9,*

    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4509-4523, 2022, DOI:10.32604/cmc.2022.025977

    Abstract Due to the difficulties of brain tumor segmentation, this paper proposes a strategy for extracting brain tumors from three-dimensional Magnetic Resonance Image (MRI) and Computed Tomography (CT) scans utilizing 3D U-Net Design and ResNet50, taken after by conventional classification strategies. In this inquire, the ResNet50 picked up accuracy with 98.96%, and the 3D U-Net scored 97.99% among the different methods of deep learning. It is to be mentioned that traditional Convolutional Neural Network (CNN) gives 97.90% accuracy on top of the 3D MRI. In expansion, the image fusion approach combines the multimodal images and makes a fused image to extricate… More >

  • Open Access


    Automated Crack Detection via Semantic Segmentation Approaches Using Advanced U-Net Architecture

    Honggeun Ji1,2, Jina Kim3, Syjung Hwang4, Eunil Park1,4,*

    Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 593-607, 2022, DOI:10.32604/iasc.2022.024405

    Abstract Cracks affect the robustness and adaptability of various infrastructures, including buildings, bridge piers, pavement, and pipelines. Therefore, the robustness and the reliability of automated crack detection are essential. In this study, we conducted image segmentation using various crack datasets by applying the advanced architecture of U-Net. First, we collected and integrated crack datasets from prior studies, including the cracks in buildings and pavements. For effective localization and detection of cracks, we used U-Net-based neural networks, ResU-Net, VGGU-Net, and EfficientU-Net. The models were evaluated by the five-fold cross-validation using several evaluation metrics including mean pixel accuracy (MPA), mean intersection over union… More >

  • Open Access


    A Convolutional Neural Network for Skin Lesion Segmentation Using Double U-Net Architecture

    Iqra Abid1, Sultan Almakdi2, Hameedur Rahman3, Ahmed Almulihi4, Ali Alqahtani2, Khairan Rajab2,5, Abdulmajeed Alqhatani2,*, Asadullah Shaikh2

    Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1407-1421, 2022, DOI:10.32604/iasc.2022.023753

    Abstract Skin lesion segmentation plays a critical role in the precise and early detection of skin cancer via recent frameworks. The prerequisite for any computer-aided skin cancer diagnosis system is the accurate segmentation of skin malignancy. To achieve this, a specialized skin image analysis technique must be used for the separation of cancerous parts from important healthy skin. This procedure is called Dermatography. Researchers have often used multiple techniques for the analysis of skin images, but, because of their low accuracy, most of these methods have turned out to be at best, inconsistent. Proper clinical treatment involves sensitivity in the surgical… More >

  • Open Access


    Enhancement of Biomass Material Characterization Images Using an Improved U-Net

    Zuozheng Lian1, Hong Zhao2,*, Qianjun Zhang1, Haizhen Wang1, E. Erdun3

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1515-1528, 2022, DOI:10.32604/cmc.2022.024779

    Abstract For scanning electron microscopes with high resolution and a strong electric field, biomass materials under observation are prone to radiation damage from the electron beam. This results in blurred or non-viable images, which affect further observation of material microscopic morphology and characterization. Restoring blurred images to their original sharpness is still a challenging problem in image processing. Traditional methods can't effectively separate image context dependency and texture information, affect the effect of image enhancement and deblurring, and are prone to gradient disappearance during model training, resulting in great difficulty in model training. In this paper, we propose the use of… More >

  • Open Access


    Mu-Net: Multi-Path Upsampling Convolution Network for Medical Image Segmentation

    Jia Chen1, Zhiqiang He1, Dayong Zhu1, Bei Hui1,*, Rita Yi Man Li2, Xiao-Guang Yue3,4,5

    CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.1, pp. 73-95, 2022, DOI:10.32604/cmes.2022.018565

    Abstract Medical image segmentation plays an important role in clinical diagnosis, quantitative analysis, and treatment process. Since 2015, U-Net-based approaches have been widely used for medical image segmentation. The purpose of the U-Net expansive path is to map low-resolution encoder feature maps to full input resolution feature maps. However, the consecutive deconvolution and convolutional operations in the expansive path lead to the loss of some high-level information. More high-level information can make the segmentation more accurate. In this paper, we propose MU-Net, a novel, multi-path upsampling convolution network to retain more high-level information. The MU-Net mainly consists of three parts: contracting… More >

  • Open Access


    Improved U-Net-Based Novel Segmentation Algorithm for Underwater Mineral Image

    Haolin Wang1, Lihui Dong1, Wei Song1,2,3,*, Xiaobin Zhao1,3, Jianxin Xia4, Tongmu Liu5

    Intelligent Automation & Soft Computing, Vol.32, No.3, pp. 1573-1586, 2022, DOI:10.32604/iasc.2022.023994

    Abstract Autonomous underwater vehicle (AUV) has many intelligent optical system, which can collect underwater signal information to make the system decision. One of them is the intelligent vision system, and it can capture the images to analyze. The performance of the particle image segmentation plays an important role in the monitoring of underwater mineral resources. In order to improve the underwater mineral image segmentation performance, some novel segmentation algorithm architectures are proposed. In this paper, an improved mineral image segmentation is proposed based on the modified U-Net. The pyramid upsampling module and residual module are bring into the U-Net model, which… More >

  • Open Access


    Make U-Net Greater: An Easy-to-Embed Approach to Improve Segmentation Performance Using Hypergraph

    Jing Peng1,2,3, Jingfu Yang2, Chaoyang Xia2, Xiaojie Li2, Yanfen Guo2, Ying Fu2, Xinlai Chen4, Zhe Cui1,3,*

    Computer Systems Science and Engineering, Vol.42, No.1, pp. 319-333, 2022, DOI:10.32604/csse.2022.022314

    Abstract Cardiac anatomy segmentation is essential for cardiomyopathy clinical diagnosis and treatment planning. Thus, accurate delineation of target volumes at risk in cardiac anatomy is important. However, manual delineation is a time-consuming and labor-intensive process for cardiologists and has been shown to lead to significant inter-and intra-practitioner variability. Thus, computer-aided or fully automatic segmentation methods are required. They can significantly economize on manpower and improve treatment efficiency. Recently, deep convolutional neural network (CNN) based methods have achieved remarkable successes in various kinds of vision tasks, such as classification, segmentation and object detection. Semantic segmentation can be considered as a pixel-wise task,… More >

  • Open Access


    Optimized U-Net Segmentation and Hybrid Res-Net for Brain Tumor MRI Images Classification

    R. Rajaragavi1,*, S. Palanivel Rajan2

    Intelligent Automation & Soft Computing, Vol.32, No.1, pp. 1-14, 2022, DOI:10.32604/iasc.2022.021206

    Abstract A brain tumor is a portion of uneven cells, need to be detected earlier for treatment. Magnetic Resonance Imaging (MRI) is a routinely utilized procedure to take brain tumor images. Manual segmentation of tumor is a crucial task and laborious. There is a need for an automated system for segmentation and classification for tumor surgery and medical treatments. This work suggests an efficient brain tumor segmentation and classification based on deep learning techniques. Initially, Squirrel search optimized bidirectional ConvLSTM U-net with attention gate proposed for brain tumour segmentation. Then, the Hybrid Deep ResNet and Inception Model used for classification. Squirrel… More >

  • Open Access


    Efficient Morphological Segmentation of Brain Hemorrhage Stroke Lesion Through MultiResUNet

    R. Shijitha1,*, P. Karthigaikumar2, A. Stanly Paul2

    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5233-5249, 2022, DOI:10.32604/cmc.2022.020227

    Abstract Brain Hemorrhagic stroke is a serious malady that is caused by the drop in blood flow through the brain and causes the brain to malfunction. Precise segmentation of brain hemorrhage is crucial, so an enhanced segmentation is carried out in this research work. The brain image of various patients has taken using an MRI scanner by the utilization of T1, T2, and FLAIR sequence. This work aims to segment the Brain Hemorrhagic stroke using deep learning-based Multi-resolution UNet (multires UNet) through morphological operations. It is hard to precisely segment the brain lesions to extract the existing region of stroke. This… More >

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