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  • Open Access

    ARTICLE

    An Improved Deep Learning Framework for Automated Optic Disc Localization and Glaucoma Detection

    Hela Elmannai1,*, Monia Hamdi1, Souham Meshoul1, Amel Ali Alhussan2, Manel Ayadi3, Amel Ksibi3

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1429-1457, 2024, DOI:10.32604/cmes.2024.048557

    Abstract Glaucoma disease causes irreversible damage to the optical nerve and it has the potential to cause permanent loss of vision. Glaucoma ranks as the second most prevalent cause of permanent blindness. Traditional glaucoma diagnosis requires a highly experienced specialist, costly equipment, and a lengthy wait time. For automatic glaucoma detection, state-of-the-art glaucoma detection methods include a segmentation-based method to calculate the cup-to-disc ratio. Other methods include multi-label segmentation networks and learning-based methods and rely on hand-crafted features. Localizing the optic disc (OD) is one of the key features in retinal images for detecting retinal diseases,… More >

  • Open Access

    ARTICLE

    Development of a Three-Dimensional Multiscale Octree SBFEM for Viscoelastic Problems of Heterogeneous Materials

    Xu Xu1, Xiaoteng Wang1, Haitian Yang1, Zhenjun Yang2, Yiqian He1,3,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1831-1861, 2024, DOI:10.32604/cmes.2024.048199

    Abstract The multiscale method provides an effective approach for the numerical analysis of heterogeneous viscoelastic materials by reducing the degree of freedoms (DOFs). A basic framework of the Multiscale Scaled Boundary Finite Element Method (MsSBFEM) was presented in our previous works, but those works only addressed two-dimensional problems. In order to solve more realistic problems, a three-dimensional MsSBFEM is further developed in this article. In the proposed method, the octree SBFEM is used to deal with the three-dimensional calculation for numerical base functions to bridge small and large scales, the three-dimensional image-based analysis can be conveniently… More >

  • Open Access

    ARTICLE

    Lineament Mapping in Batie Area (West-Cameroon) Using Landsat-9 Operational Land Imager/Thermal Infrared Sensor and Shuttle Radar Topography Mission Data: Hydrogeological Implication

    Jean Aime Mono1,2,*, Apollinaire Bouba3, Jean Daniel Ngoh4, Olivier Ulrich Igor Owono Amougou5, Françoise Martine Enyegue A Nyam6, Théophile Ndougsa Mbarga7

    Revue Internationale de Géomatique, Vol.33, pp. 135-154, 2024, DOI:10.32604/rig.2024.049966

    Abstract This study focuses on the mapping of lineaments using remote sensing techniques and Geographic Information Systems. The aim is to carry out a statistical analysis of the lineaments in order to better understand the organization of fracturing in the Batie district, and to identify areas of high fracturing density and their relationship with the hydrographic network. The methodology implemented to achieve these objectives is based on the processing and analysis of Landsat 9 Operational Land Imager/Thermal Infrared Sensor (OLI/TIRS) satellite imagery and Shuttle Radar Topography Mission (SRTM) data covering the study area. After essential pre-processing… More >

  • Open Access

    ARTICLE

    Transformation of MRI Images to Three-Level Color Spaces for Brain Tumor Classification Using Deep-Net

    Fadl Dahan*

    Intelligent Automation & Soft Computing, Vol.39, No.2, pp. 381-395, 2024, DOI:10.32604/iasc.2024.047921

    Abstract In the domain of medical imaging, the accurate detection and classification of brain tumors is very important. This study introduces an advanced method for identifying camouflaged brain tumors within images. Our proposed model consists of three steps: Feature extraction, feature fusion, and then classification. The core of this model revolves around a feature extraction framework that combines color-transformed images with deep learning techniques, using the ResNet50 Convolutional Neural Network (CNN) architecture. So the focus is to extract robust feature from MRI images, particularly emphasizing weighted average features extracted from the first convolutional layer renowned for… More >

  • Open Access

    ARTICLE

    Predicting 3D Radiotherapy Dose-Volume Based on Deep Learning

    Do Nang Toan1,*, Lam Thanh Hien2, Ha Manh Toan1, Nguyen Trong Vinh2, Pham Trung Hieu1

    Intelligent Automation & Soft Computing, Vol.39, No.2, pp. 319-335, 2024, DOI:10.32604/iasc.2024.046925

    Abstract Cancer is one of the most dangerous diseases with high mortality. One of the principal treatments is radiotherapy by using radiation beams to destroy cancer cells and this workflow requires a lot of experience and skill from doctors and technicians. In our study, we focused on the 3D dose prediction problem in radiotherapy by applying the deep-learning approach to computed tomography (CT) images of cancer patients. Medical image data has more complex characteristics than normal image data, and this research aims to explore the effectiveness of data preprocessing and augmentation in the context of the… More >

  • Open Access

    ARTICLE

    Tuberculosis Diagnosis and Visualization with a Large Vietnamese X-Ray Image Dataset

    Nguyen Trong Vinh1, Lam Thanh Hien1, Ha Manh Toan2, Ngo Duc Vinh3, Do Nang Toan2,*

    Intelligent Automation & Soft Computing, Vol.39, No.2, pp. 281-299, 2024, DOI:10.32604/iasc.2024.045297

    Abstract Tuberculosis is a dangerous disease to human life, and we need a lot of attempts to stop and reverse it. Significantly, in the COVID-19 pandemic, access to medical services for tuberculosis has become very difficult. The late detection of tuberculosis could lead to danger to patient health, even death. Vietnam is one of the countries heavily affected by the COVID-19 pandemic, and many residential areas as well as hospitals have to be isolated for a long time. Reality demands a fast and effective tuberculosis diagnosis solution to deal with the difficulty of accessing medical services,… More >

  • Open Access

    ARTICLE

    Spatial and Contextual Path Network for Image Inpainting

    Dengyong Zhang1,2, Yuting Zhao1,2, Feng Li1,2, Arun Kumar Sangaiah3,4,*

    Intelligent Automation & Soft Computing, Vol.39, No.2, pp. 115-133, 2024, DOI:10.32604/iasc.2024.040847

    Abstract Image inpainting is a kind of use known area of information technology to repair the loss or damage to the area. Image feature extraction is the core of image restoration. Getting enough space for information and a larger receptive field is very important to realize high-precision image inpainting. However, in the process of feature extraction, it is difficult to meet the two requirements of obtaining sufficient spatial information and large receptive fields at the same time. In order to obtain more spatial information and a larger receptive field at the same time, we put forward… More >

  • Open Access

    ARTICLE

    Cross-Modal Consistency with Aesthetic Similarity for Multimodal False Information Detection

    Weijian Fan1,*, Ziwei Shi2

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2723-2741, 2024, DOI:10.32604/cmc.2024.050344

    Abstract With the explosive growth of false information on social media platforms, the automatic detection of multimodal false information has received increasing attention. Recent research has significantly contributed to multimodal information exchange and fusion, with many methods attempting to integrate unimodal features to generate multimodal news representations. However, they still need to fully explore the hierarchical and complex semantic correlations between different modal contents, severely limiting their performance detecting multimodal false information. This work proposes a two-stage detection framework for multimodal false information detection, called ASMFD, which is based on image aesthetic similarity to segment and… More >

  • Open Access

    ARTICLE

    Chaotic CS Encryption: An Efficient Image Encryption Algorithm Based on Chebyshev Chaotic System and Compressive Sensing

    Mingliang Sun, Jie Yuan*, Xiaoyong Li, Dongxiao Liu

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2625-2646, 2024, DOI:10.32604/cmc.2024.050337

    Abstract Images are the most important carrier of human information. Moreover, how to safely transmit digital images through public channels has become an urgent problem. In this paper, we propose a novel image encryption algorithm, called chaotic compressive sensing (CS) encryption (CCSE), which can not only improve the efficiency of image transmission but also introduce the high security of the chaotic system. Specifically, the proposed CCSE can fully leverage the advantages of the Chebyshev chaotic system and CS, enabling it to withstand various attacks, such as differential attacks, and exhibit robustness. First, we use a sparse… More >

  • Open Access

    ARTICLE

    Monocular Distance Estimated Based on PTZ Camera

    Qirui Zhong1, Xiaogang Cheng2,*, Yuxin Song3, Han Wang2

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 3417-3433, 2024, DOI:10.32604/cmc.2024.049992

    Abstract This paper introduces an intelligent computational approach for extracting salient objects from images and estimating their distance information with PTZ (Pan-Tilt-Zoom) cameras. PTZ cameras have found wide applications in numerous public places, serving various purposes such as public security management, natural disaster monitoring, and crisis alarms, particularly with the rapid development of Artificial Intelligence and global infrastructural projects. In this paper, we combine Gauss optical principles with the PTZ camera’s capabilities of horizontal and pitch rotation, as well as optical zoom, to estimate the distance of the object. We present a novel monocular object distance… More >

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