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  • Open Access

    ARTICLE

    Multiscale and Auto-Tuned Semi-Supervised Deep Subspace Clustering and Its Application in Brain Tumor Clustering

    Zhenyu Qian1, Yizhang Jiang1, Zhou Hong1, Lijun Huang2, Fengda Li3, KhinWee Lai6, Kaijian Xia4,5,6,*

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4741-4762, 2024, DOI:10.32604/cmc.2024.050920

    Abstract In this paper, we introduce a novel Multi-scale and Auto-tuned Semi-supervised Deep Subspace Clustering (MAS-DSC) algorithm, aimed at addressing the challenges of deep subspace clustering in high-dimensional real-world data, particularly in the field of medical imaging. Traditional deep subspace clustering algorithms, which are mostly unsupervised, are limited in their ability to effectively utilize the inherent prior knowledge in medical images. Our MAS-DSC algorithm incorporates a semi-supervised learning framework that uses a small amount of labeled data to guide the clustering process, thereby enhancing the discriminative power of the feature representations. Additionally, the multi-scale feature extraction… More > Graphic Abstract

    Multiscale and Auto-Tuned Semi-Supervised Deep Subspace Clustering and Its Application in Brain Tumor Clustering

  • Open Access

    ARTICLE

    Fine-Grained Ship Recognition Based on Visible and Near-Infrared Multimodal Remote Sensing Images: Dataset, Methodology and Evaluation

    Shiwen Song, Rui Zhang, Min Hu*, Feiyao Huang

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 5243-5271, 2024, DOI:10.32604/cmc.2024.050879

    Abstract Fine-grained recognition of ships based on remote sensing images is crucial to safeguarding maritime rights and interests and maintaining national security. Currently, with the emergence of massive high-resolution multi-modality images, the use of multi-modality images for fine-grained recognition has become a promising technology. Fine-grained recognition of multi-modality images imposes higher requirements on the dataset samples. The key to the problem is how to extract and fuse the complementary features of multi-modality images to obtain more discriminative fusion features. The attention mechanism helps the model to pinpoint the key information in the image, resulting in a… More >

  • Open Access

    ARTICLE

    MSD-Net: Pneumonia Classification Model Based on Multi-Scale Directional Feature Enhancement

    Tao Zhou1,3, Yujie Guo1,3,*, Caiyue Peng1,3, Yuxia Niu1,3, Yunfeng Pan1,3, Huiling Lu2

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4863-4882, 2024, DOI:10.32604/cmc.2024.050767

    Abstract Computer-aided diagnosis of pneumonia based on deep learning is a research hotspot. However, there are some problems that the features of different sizes and different directions are not sufficient when extracting the features in lung X-ray images. A pneumonia classification model based on multi-scale directional feature enhancement MSD-Net is proposed in this paper. The main innovations are as follows: Firstly, the Multi-scale Residual Feature Extraction Module (MRFEM) is designed to effectively extract multi-scale features. The MRFEM uses dilated convolutions with different expansion rates to increase the receptive field and extract multi-scale features effectively. Secondly, the… More >

  • Open Access

    ARTICLE

    Exploring Motor Imagery EEG: Enhanced EEG Microstate Analysis with GMD-Driven Density Canopy Method

    Xin Xiong1, Jing Zhang1, Sanli Yi1, Chunwu Wang2, Ruixiang Liu3, Jianfeng He1,*

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4659-4681, 2024, DOI:10.32604/cmc.2024.050528

    Abstract The analysis of microstates in EEG signals is a crucial technique for understanding the spatiotemporal dynamics of brain electrical activity. Traditional methods such as Atomic Agglomerative Hierarchical Clustering (AAHC), K-means clustering, Principal Component Analysis (PCA), and Independent Component Analysis (ICA) are limited by a fixed number of microstate maps and insufficient capability in cross-task feature extraction. Tackling these limitations, this study introduces a Global Map Dissimilarity (GMD)-driven density canopy K-means clustering algorithm. This innovative approach autonomously determines the optimal number of EEG microstate topographies and employs Gaussian kernel density estimation alongside the GMD index for… More >

  • Open Access

    ARTICLE

    Research on Sarcasm Detection Technology Based on Image-Text Fusion

    Xiaofang Jin1, Yuying Yang1,*, Yinan Wu1, Ying Xu2

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 5225-5242, 2024, DOI:10.32604/cmc.2024.050384

    Abstract The emergence of new media in various fields has continuously strengthened the social aspect of social media. Netizens tend to express emotions in social interactions, and many people even use satire, metaphors, and other techniques to express some negative emotions, it is necessary to detect sarcasm in social comment data. For sarcasm, the more reference data modalities used, the better the experimental effect. This paper conducts research on sarcasm detection technology based on image-text fusion data. To effectively utilize the features of each modality, a feature reconstruction output algorithm is proposed. This algorithm is based… More >

  • Open Access

    ARTICLE

    Enhancing Hyper-Spectral Image Classification with Reinforcement Learning and Advanced Multi-Objective Binary Grey Wolf Optimization

    Mehrdad Shoeibi1, Mohammad Mehdi Sharifi Nevisi2, Reza Salehi3, Diego Martín3,*, Zahra Halimi4, Sahba Baniasadi5

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 3469-3493, 2024, DOI:10.32604/cmc.2024.049847

    Abstract Hyperspectral (HS) image classification plays a crucial role in numerous areas including remote sensing (RS), agriculture, and the monitoring of the environment. Optimal band selection in HS images is crucial for improving the efficiency and accuracy of image classification. This process involves selecting the most informative spectral bands, which leads to a reduction in data volume. Focusing on these key bands also enhances the accuracy of classification algorithms, as redundant or irrelevant bands, which can introduce noise and lower model performance, are excluded. In this paper, we propose an approach for HS image classification using… More >

  • Open Access

    ARTICLE

    An Improved UNet Lightweight Network for Semantic Segmentation of Weed Images in Corn Fields

    Yu Zuo1, Wenwen Li2,*

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4413-4431, 2024, DOI:10.32604/cmc.2024.049805

    Abstract In cornfields, factors such as the similarity between corn seedlings and weeds and the blurring of plant edge details pose challenges to corn and weed segmentation. In addition, remote areas such as farmland are usually constrained by limited computational resources and limited collected data. Therefore, it becomes necessary to lighten the model to better adapt to complex cornfield scene, and make full use of the limited data information. In this paper, we propose an improved image segmentation algorithm based on unet. Firstly, the inverted residual structure is introduced into the contraction path to reduce the… More >

  • Open Access

    ARTICLE

    CrossLinkNet: An Explainable and Trustworthy AI Framework for Whole-Slide Images Segmentation

    Peng Xiao1, Qi Zhong2, Jingxue Chen1, Dongyuan Wu1, Zhen Qin1, Erqiang Zhou1,*

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4703-4724, 2024, DOI:10.32604/cmc.2024.049791

    Abstract In the intelligent medical diagnosis area, Artificial Intelligence (AI)’s trustworthiness, reliability, and interpretability are critical, especially in cancer diagnosis. Traditional neural networks, while excellent at processing natural images, often lack interpretability and adaptability when processing high-resolution digital pathological images. This limitation is particularly evident in pathological diagnosis, which is the gold standard of cancer diagnosis and relies on a pathologist’s careful examination and analysis of digital pathological slides to identify the features and progression of the disease. Therefore, the integration of interpretable AI into smart medical diagnosis is not only an inevitable technological trend but… More >

  • Open Access

    ARTICLE

    An Interactive Collaborative Creation System for Shadow Puppets Based on Smooth Generative Adversarial Networks

    Cheng Yang1,2, Miaojia Lou2,*, Xiaoyu Chen1,2, Zixuan Ren1

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4107-4126, 2024, DOI:10.32604/cmc.2024.049183

    Abstract Chinese shadow puppetry has been recognized as a world intangible cultural heritage. However, it faces substantial challenges in its preservation and advancement due to the intricate and labor-intensive nature of crafting shadow puppets. To ensure the inheritance and development of this cultural heritage, it is imperative to enable traditional art to flourish in the digital era. This paper presents an Interactive Collaborative Creation System for shadow puppets, designed to facilitate the creation of high-quality shadow puppet images with greater ease. The system comprises four key functions: Image contour extraction, intelligent reference recommendation, generation network, and… More >

  • Open Access

    ARTICLE

    Simulation of Fracture Process of Lightweight Aggregate Concrete Based on Digital Image Processing Technology

    Safwan Al-sayed, Xi Wang, Yijiang Peng*

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4169-4195, 2024, DOI:10.32604/cmc.2024.048916

    Abstract The mechanical properties and failure mechanism of lightweight aggregate concrete (LWAC) is a hot topic in the engineering field, and the relationship between its microstructure and macroscopic mechanical properties is also a frontier research topic in the academic field. In this study, the image processing technology is used to establish a micro-structure model of lightweight aggregate concrete. Through the information extraction and processing of the section image of actual light aggregate concrete specimens, the mesostructural model of light aggregate concrete with real aggregate characteristics is established. The numerical simulation of uniaxial tensile test, uniaxial compression… More >

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