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  • Open Access

    ARTICLE

    Research on thyroid nodule segmentation using an improved U-Net network

    Peng Xu1

    Revista Internacional de Métodos Numéricos para Cálculo y Diseño en Ingeniería, Vol.40, No.2, pp. 1-7, 2024, DOI:10.23967/j.rimni.2024.05.012 - 06 June 2024

    Abstract To develop a precise neural network model designed for segmenting ultrasound images of thyroid nodules. The deep learning U-Net network model was utilized as the main backbone, with improvements made to the convolutional operations and the implementation of multilayer perceptron modeling at the lower levels, using the more effective BCEDice loss function. The modified network achieved enhanced segmentation precision and robust generalization capabilities, with a Dice coefficient of 0.9062, precision of 0.9153, recall of 0.9023, and an F1 score of 0.9062, indicating improvements over the U-Net and Swin-Unet to various extents. The U-Net network enhancement More >

  • Open Access

    ARTICLE

    Guided-YNet: Saliency Feature-Guided Interactive Feature Enhancement Lung Tumor Segmentation Network

    Tao Zhou1,3, Yunfeng Pan1,3,*, Huiling Lu2, Pei Dang1,3, Yujie Guo1,3, Yaxing Wang1,3

    CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4813-4832, 2024, DOI:10.32604/cmc.2024.054685 - 12 September 2024

    Abstract Multimodal lung tumor medical images can provide anatomical and functional information for the same lesion. Such as Positron Emission Computed Tomography (PET), Computed Tomography (CT), and PET-CT. How to utilize the lesion anatomical and functional information effectively and improve the network segmentation performance are key questions. To solve the problem, the Saliency Feature-Guided Interactive Feature Enhancement Lung Tumor Segmentation Network (Guide-YNet) is proposed in this paper. Firstly, a double-encoder single-decoder U-Net is used as the backbone in this model, a single-coder single-decoder U-Net is used to generate the saliency guided feature using PET image and… More >

  • Open Access

    ARTICLE

    Multi-Level Image Segmentation Combining Chaotic Initialized Chimp Optimization Algorithm and Cauchy Mutation

    Shujing Li, Zhangfei Li, Wenhui Cheng, Chenyang Qi, Linguo Li*

    CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2049-2063, 2024, DOI:10.32604/cmc.2024.051928 - 15 August 2024

    Abstract To enhance the diversity and distribution uniformity of initial population, as well as to avoid local extrema in the Chimp Optimization Algorithm (CHOA), this paper improves the CHOA based on chaos initialization and Cauchy mutation. First, Sin chaos is introduced to improve the random population initialization scheme of the CHOA, which not only guarantees the diversity of the population, but also enhances the distribution uniformity of the initial population. Next, Cauchy mutation is added to optimize the global search ability of the CHOA in the process of position (threshold) updating to avoid the CHOA falling More >

  • Open Access

    ARTICLE

    Transformer-Based Cloud Detection Method for High-Resolution Remote Sensing Imagery

    Haotang Tan1, Song Sun2,*, Tian Cheng3, Xiyuan Shu2

    CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 661-678, 2024, DOI:10.32604/cmc.2024.052208 - 18 July 2024

    Abstract Cloud detection from satellite and drone imagery is crucial for applications such as weather forecasting and environmental monitoring. Addressing the limitations of conventional convolutional neural networks, we propose an innovative transformer-based method. This method leverages transformers, which are adept at processing data sequences, to enhance cloud detection accuracy. Additionally, we introduce a Cyclic Refinement Architecture that improves the resolution and quality of feature extraction, thereby aiding in the retention of critical details often lost during cloud detection. Our extensive experimental validation shows that our approach significantly outperforms established models, excelling in high-resolution feature extraction and More >

  • Open Access

    ARTICLE

    Image Segmentation-P300 Selector: A Brain–Computer Interface System for Target Selection

    Hang Sun, Changsheng Li*, He Zhang

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2505-2522, 2024, DOI:10.32604/cmc.2024.049898 - 15 May 2024

    Abstract Brain–computer interface (BCI) systems, such as the P300 speller, enable patients to express intentions without necessitating extensive training. However, the complexity of operational instructions and the slow pace of character spelling pose challenges for some patients. In this paper, an image segmentation P300 selector based on YOLOv7-mask and DeepSORT is proposed. The proposed system utilizes a camera to capture real-world objects for classification and tracking. By applying predefined stimulation rules and object-specific masks, the proposed system triggers stimuli associated with the objects displayed on the screen, inducing the generation of P300 signals in the patient’s… More >

  • Open Access

    ARTICLE

    Low-Brightness Object Recognition Based on Deep Learning

    Shu-Yin Chiang*, Ting-Yu Lin

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 1757-1773, 2024, DOI:10.32604/cmc.2024.049477 - 15 May 2024

    Abstract This research focuses on addressing the challenges associated with image detection in low-light environments, particularly by applying artificial intelligence techniques to machine vision and object recognition systems. The primary goal is to tackle issues related to recognizing objects with low brightness levels. In this study, the Intel RealSense Lidar Camera L515 is used to simultaneously capture color information and 16-bit depth information images. The detection scenarios are categorized into normal brightness and low brightness situations. When the system determines a normal brightness environment, normal brightness images are recognized using deep learning methods. In low-brightness situations,… More >

  • Open Access

    ARTICLE

    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 - 16 April 2024

    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

    ARTICLE

    An Efficient Local Radial Basis Function Method for Image Segmentation Based on the Chan–Vese Model

    Shupeng Qiu, Chujin Lin, Wei Zhao*

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 1119-1134, 2024, DOI:10.32604/cmes.2023.030915 - 30 December 2023

    Abstract In this paper, we consider the Chan–Vese (C-V) model for image segmentation and obtain its numerical solution accurately and efficiently. For this purpose, we present a local radial basis function method based on a Gaussian kernel (GA-LRBF) for spatial discretization. Compared to the standard radial basis function method, this approach consumes less CPU time and maintains good stability because it uses only a small subset of points in the whole computational domain. Additionally, since the Gaussian function has the property of dimensional separation, the GA-LRBF method is suitable for dealing with isotropic images. Finally, a More > Graphic Abstract

    An Efficient Local Radial Basis Function Method for Image Segmentation Based on the Chan–Vese Model

  • Open Access

    ARTICLE

    Fuzzy Difference Equations in Diagnoses of Glaucoma from Retinal Images Using Deep Learning

    D. Dorathy Prema Kavitha1, L. Francis Raj1, Sandeep Kautish2,#, Abdulaziz S. Almazyad3, Karam M. Sallam4, Ali Wagdy Mohamed5,6,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 801-816, 2024, DOI:10.32604/cmes.2023.030902 - 30 December 2023

    Abstract The intuitive fuzzy set has found important application in decision-making and machine learning. To enrich and utilize the intuitive fuzzy set, this study designed and developed a deep neural network-based glaucoma eye detection using fuzzy difference equations in the domain where the retinal images converge. Retinal image detections are categorized as normal eye recognition, suspected glaucomatous eye recognition, and glaucomatous eye recognition. Fuzzy degrees associated with weighted values are calculated to determine the level of concentration between the fuzzy partition and the retinal images. The proposed model was used to diagnose glaucoma using retinal images… More >

  • Open Access

    ARTICLE

    An Intelligent Sensor Data Preprocessing Method for OCT Fundus Image Watermarking Using an RCNN

    Jialun Lin1, Qiong Chen1,2,3,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1549-1561, 2024, DOI:10.32604/cmes.2023.029631 - 17 November 2023

    Abstract Watermarks can provide reliable and secure copyright protection for optical coherence tomography (OCT) fundus images. The effective image segmentation is helpful for promoting OCT image watermarking. However, OCT images have a large amount of low-quality data, which seriously affects the performance of segmentation methods. Therefore, this paper proposes an effective segmentation method for OCT fundus image watermarking using a rough convolutional neural network (RCNN). First, the rough-set-based feature discretization module is designed to preprocess the input data. Second, a dual attention mechanism for feature channels and spatial regions in the CNN is added to enable… More >

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