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  • Open Access

    ARTICLE

    Learning Discriminatory Information for Object Detection on Urine Sediment Image

    Sixian Chan1,2, Binghui Wu1, Guodao Zhang3, Yuan Yao4, Hongqiang Wang2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 411-428, 2024, DOI:10.32604/cmes.2023.029485

    Abstract In clinical practice, the microscopic examination of urine sediment is considered an important in vitro examination with many broad applications. Measuring the amount of each type of urine sediment allows for screening, diagnosis and evaluation of kidney and urinary tract disease, providing insight into the specific type and severity. However, manual urine sediment examination is labor-intensive, time-consuming, and subjective. Traditional machine learning based object detection methods require hand-crafted features for localization and classification, which have poor generalization capabilities and are difficult to quickly and accurately detect the number of urine sediments. Deep learning based object detection methods have the potential… More > Graphic Abstract

    Learning Discriminatory Information for Object Detection on Urine Sediment Image

  • Open Access

    ARTICLE

    A Degradation Type Adaptive and Deep CNN-Based Image Classification Model for Degraded Images

    Huanhua Liu, Wei Wang*, Hanyu Liu, Shuheng Yi, Yonghao Yu, Xunwen Yao

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 459-472, 2024, DOI:10.32604/cmes.2023.029084

    Abstract Deep Convolutional Neural Networks (CNNs) have achieved high accuracy in image classification tasks, however, most existing models are trained on high-quality images that are not subject to image degradation. In practice, images are often affected by various types of degradation which can significantly impact the performance of CNNs. In this work, we investigate the influence of image degradation on three typical image classification CNNs and propose a Degradation Type Adaptive Image Classification Model (DTA-ICM) to improve the existing CNNs’ classification accuracy on degraded images. The proposed DTA-ICM comprises two key components: a Degradation Type Predictor (DTP) and a Degradation Type… More >

  • Open Access

    ARTICLE

    PanopticUAV: Panoptic Segmentation of UAV Images for Marine Environment Monitoring

    Yuling Dou1, Fengqin Yao1, Xiandong Wang1, Liang Qu2, Long Chen3, Zhiwei Xu4, Laihui Ding4, Leon Bevan Bullock1, Guoqiang Zhong1, Shengke Wang1,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 1001-1014, 2024, DOI:10.32604/cmes.2023.027764

    Abstract UAV marine monitoring plays an essential role in marine environmental protection because of its flexibility and convenience, low cost and convenient maintenance. In marine environmental monitoring, the similarity between objects such as oil spill and sea surface, Spartina alterniflora and algae is high, and the effect of the general segmentation algorithm is poor, which brings new challenges to the segmentation of UAV marine images. Panoramic segmentation can do object detection and semantic segmentation at the same time, which can well solve the polymorphism problem of objects in UAV ocean images. Currently, there are few studies on UAV marine image recognition… More >

  • Open Access

    ARTICLE

    SC-Net: A New U-Net Network for Hippocampus Segmentation

    Xinyi Xiao, Dongbo Pan*, Jianjun Yuan

    Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 3179-3191, 2023, DOI:10.32604/iasc.2023.041208

    Abstract Neurological disorders like Alzheimer’s disease have a significant impact on the lives and health of the elderly as the aging population continues to grow. Doctors can achieve effective prevention and treatment of Alzheimer’s disease according to the morphological volume of hippocampus. General segmentation techniques frequently fail to produce satisfactory results due to hippocampus’s small size, complex structure, and fuzzy edges. We develop a new SC-Net model using complete brain MRI images to achieve high-precision segmentation of hippocampal structures. The proposed network improves the accuracy of hippocampal structural segmentation by retaining the original location information of the hippocampus. Extensive experimental results… More >

  • Open Access

    ARTICLE

    An Automatic Classification Grading of Spinach Seedlings Water Stress Based on N-MobileNetXt

    Yanlei Xu, Xue Cong, Yuting Zhai, Zhiyuan Gao, Helong Yu*

    Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 3019-3037, 2023, DOI:10.32604/iasc.2023.040330

    Abstract To solve inefficient water stress classification of spinach seedlings under complex background, this study proposed an automatic classification method for the water stress level of spinach seedlings based on the N-MobileNetXt (NCAM+MobileNetXt) network. Firstly, this study reconstructed the Sandglass Block to effectively increase the model accuracy; secondly, this study introduced the group convolution module and a two-dimensional adaptive average pool, which can significantly compress the model parameters and enhance the model robustness separately; finally, this study innovatively proposed the Normalization-based Channel Attention Module (NCAM) to enhance the image features obviously. The experimental results showed that the classification accuracy of N-MobileNetXt… More >

  • Open Access

    ARTICLE

    A New Method for Image Tamper Detection Based on an Improved U-Net

    Jie Zhang, Jianxun Zhang*, Bowen Li, Jie Cao, Yifan Guo

    Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2883-2895, 2023, DOI:10.32604/iasc.2023.039805

    Abstract With the improvement of image editing technology, the threshold of image tampering technology decreases, which leads to a decrease in the authenticity of image content. This has also driven research on image forgery detection techniques. In this paper, a U-Net with multiple sensory field feature extraction (MSCU-Net) for image forgery detection is proposed. The proposed MSCU-Net is an end-to-end image essential attribute segmentation network that can perform image forgery detection without any pre-processing or post-processing. MSCU-Net replaces the single-scale convolution module in the original network with an improved multiple perceptual field convolution module so that the decoder can synthesize the… More >

  • Open Access

    ARTICLE

    A Sketch-Based Generation Model for Diverse Ceramic Tile Images Using Generative Adversarial Network

    Jianfeng Lu1,*, Xinyi Liu1, Mengtao Shi1, Chen Cui1,2, Mahmoud Emam1,3

    Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2865-2882, 2023, DOI:10.32604/iasc.2023.039742

    Abstract Ceramic tiles are one of the most indispensable materials for interior decoration. The ceramic patterns can’t match the design requirements in terms of diversity and interactivity due to their natural textures. In this paper, we propose a sketch-based generation method for generating diverse ceramic tile images based on a hand-drawn sketches using Generative Adversarial Network (GAN). The generated tile images can be tailored to meet the specific needs of the user for the tile textures. The proposed method consists of four steps. Firstly, a dataset of ceramic tile images with diverse distributions is created and then pre-trained based on GAN.… More >

  • Open Access

    ARTICLE

    A Novel Parallel Computing Confidentiality Scheme Based on Hindmarsh-Rose Model

    Jawad Ahmad1,*, Mimonah Al Qathrady2, Mohammed S. Alshehri3, Yazeed Yasin Ghadi4, Mujeeb Ur Rehman5, Syed Aziz Shah6

    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1325-1341, 2023, DOI:10.32604/cmc.2023.040858

    Abstract Due to the inherent insecure nature of the Internet, it is crucial to ensure the secure transmission of image data over this network. Additionally, given the limitations of computers, it becomes even more important to employ efficient and fast image encryption techniques. While 1D chaotic maps offer a practical approach to real-time image encryption, their limited flexibility and increased vulnerability restrict their practical application. In this research, we have utilized a 3D Hindmarsh-Rose model to construct a secure cryptosystem. The randomness of the chaotic map is assessed through standard analysis. The proposed system enhances security by incorporating an increased number… More >

  • Open Access

    ARTICLE

    PLDMLT: Multi-Task Learning of Diabetic Retinopathy Using the Pixel-Level Labeled Fundus Images

    Hengyang Liu, Chuncheng Huang*

    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1745-1761, 2023, DOI:10.32604/cmc.2023.040710

    Abstract In the field of medical images, pixel-level labels are time-consuming and expensive to acquire, while image-level labels are relatively easier to obtain. Therefore, it makes sense to learn more information (knowledge) from a small number of hard-to-get pixel-level annotated images to apply to different tasks to maximize their usefulness and save time and training costs. In this paper, using Pixel-Level Labeled Images for Multi-Task Learning (PLDMLT), we focus on grading the severity of fundus images for Diabetic Retinopathy (DR). This is because, for the segmentation task, there is a finely labeled mask, while the severity grading task is without classification… More >

  • Open Access

    ARTICLE

    Asymmetric Key Cryptosystem for Image Encryption by Elliptic Curve over Galois Field

    Mohammad Mazyad Hazzazi1, Hafeez Ur Rehman2,*, Tariq Shah2, Hajra Younas2

    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2033-2060, 2023, DOI:10.32604/cmc.2023.040629

    Abstract Protecting the integrity and secrecy of digital data transmitted through the internet is a growing problem. In this paper, we introduce an asymmetric key algorithm for specifically processing images with larger bit values. To overcome the separate flaws of elliptic curve cryptography (ECC) and the Hill cipher (HC), we present an approach to picture encryption by combining these two encryption approaches. In addition, to strengthen our scheme, the group laws are defined over the rational points of a given elliptic curve (EC) over a Galois field (GF). The exclusive-or (XOR) function is used instead of matrix multiplication to encrypt and… More >

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