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  • Open Access

    ARTICLE

    Deep Learning for Image Segmentation: A Focus on Medical Imaging

    Ali F. Khalifa1, Eman Badr1,2,*

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1995-2024, 2023, DOI:10.32604/cmc.2023.035888

    Abstract Image segmentation is crucial for various research areas. Many computer vision applications depend on segmenting images to understand the scene, such as autonomous driving, surveillance systems, robotics, and medical imaging. With the recent advances in deep learning (DL) and its confounding results in image segmentation, more attention has been drawn to its use in medical image segmentation. This article introduces a survey of the state-of-the-art deep convolution neural network (CNN) models and mechanisms utilized in image segmentation. First, segmentation models are categorized based on their model architecture and primary working principle. Then, CNN categories are More >

  • Open Access

    ARTICLE

    An Intelligent Decision Support System for Lung Cancer Diagnosis

    Ahmed A. Alsheikhy1,*, Yahia F. Said1, Tawfeeq Shawly2

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 799-817, 2023, DOI:10.32604/csse.2023.035269

    Abstract Lung cancer is the leading cause of cancer-related death around the globe. The treatment and survival rates among lung cancer patients are significantly impacted by early diagnosis. Most diagnostic techniques can identify and classify only one type of lung cancer. It is crucial to close this gap with a system that detects all lung cancer types. This paper proposes an intelligent decision support system for this purpose. This system aims to support the quick and early detection and classification of all lung cancer types and subtypes to improve treatment and save lives. Its algorithm uses… More >

  • Open Access

    ARTICLE

    Vessels Segmentation in Angiograms Using Convolutional Neural Network: A Deep Learning Based Approach

    Sanjiban Sekhar Roy1, Ching-Hsien Hsu2,3,4,*, Akash Samaran1, Ranjan Goyal1, Arindam Pande5, Valentina E. Balas6

    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.1, pp. 241-255, 2023, DOI:10.32604/cmes.2023.019644

    Abstract Coronary artery disease (CAD) has become a significant cause of heart attack, especially among those 40 years old or younger. There is a need to develop new technologies and methods to deal with this disease. Many researchers have proposed image processing-based solutions for CAD diagnosis, but achieving highly accurate results for angiogram segmentation is still a challenge. Several different types of angiograms are adopted for CAD diagnosis. This paper proposes an approach for image segmentation using Convolution Neural Networks (CNN) for diagnosing coronary artery disease to achieve state-of-the-art results. We have collected the 2D X-ray… More >

  • Open Access

    ARTICLE

    Fast Segmentation Method of Sonar Images for Jacket Installation Environment

    Hande Mao1,2, Hongzhe Yan1, Lei Lin1, Wentao Dong1,3, Yuhang Li1, Yuliang Liu2,4,*, Jing Xue5

    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1671-1686, 2023, DOI:10.32604/iasc.2023.028819

    Abstract It has remained a hard nut for years to segment sonar images of jacket installation environment, most of which are noisy images with inevitable blur after noise reduction. For the purpose of solutions to this problem, a fast segmentation algorithm is proposed on the basis of the gray value characteristics of sonar images. This algorithm is endowed with the advantage in no need of segmentation thresholds. To realize this goal, we follow the undermentioned steps: first, calculate the gray matrix of the fuzzy image background. After adjusting the gray value, the image is divided into More >

  • Open Access

    ARTICLE

    Fusion Strategy for Improving Medical Image Segmentation

    Fahad Alraddady1, E. A. Zanaty2, Aida H. Abu bakr3, Walaa M. Abd-Elhafiez4,5,*

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3627-3646, 2023, DOI:10.32604/cmc.2023.027606

    Abstract In this paper, we combine decision fusion methods with four meta-heuristic algorithms (Particle Swarm Optimization (PSO) algorithm, Cuckoo search algorithm, modification of Cuckoo Search (CS McCulloch) algorithm and Genetic algorithm) in order to improve the image segmentation. The proposed technique based on fusing the data from Particle Swarm Optimization (PSO), Cuckoo search, modification of Cuckoo Search (CS McCulloch) and Genetic algorithms are obtained for improving magnetic resonance images (MRIs) segmentation. Four algorithms are used to compute the accuracy of each method while the outputs are passed to fusion methods. In order to obtain parts of More >

  • Open Access

    ARTICLE

    A Road Segmentation Model Based on Mixture of the Convolutional Neural Network and the Transformer Network

    Fenglei Xu#, Haokai Zhao#, Fuyuan Hu*, Mingfei Shen, Yifei Wu

    CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.2, pp. 1559-1570, 2023, DOI:10.32604/cmes.2022.023217

    Abstract Convolutional neural networks (CNN) based on U-shaped structures and skip connections play a pivotal role in various image segmentation tasks. Recently, Transformer starts to lead new trends in the image segmentation task. Transformer layer can construct the relationship between all pixels, and the two parties can complement each other well. On the basis of these characteristics, we try to combine Transformer pipeline and convolutional neural network pipeline to gain the advantages of both. The image is put into the U-shaped encoder-decoder architecture based on empirical combination of self-attention and convolution, in which skip connections are More >

  • Open Access

    ARTICLE

    An Interpretable CNN for the Segmentation of the Left Ventricle in Cardiac MRI by Real-Time Visualization

    Jun Liu1, Geng Yuan2, Changdi Yang2, Houbing Song3, Liang Luo4,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.2, pp. 1571-1587, 2023, DOI:10.32604/cmes.2022.023195

    Abstract The interpretability of deep learning models has emerged as a compelling area in artificial intelligence research. The safety criteria for medical imaging are highly stringent, and models are required for an explanation. However, existing convolutional neural network solutions for left ventricular segmentation are viewed in terms of inputs and outputs. Thus, the interpretability of CNNs has come into the spotlight. Since medical imaging data are limited, many methods to fine-tune medical imaging models that are popular in transfer models have been built using massive public ImageNet datasets by the transfer learning method. Unfortunately, this generates… More >

  • Open Access

    ARTICLE

    Automatic Image Annotation Using Adaptive Convolutional Deep Learning Model

    R. Jayaraj1,*, S. Lokesh2

    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 481-497, 2023, DOI:10.32604/iasc.2023.030495

    Abstract Every day, websites and personal archives create more and more photos. The size of these archives is immeasurable. The comfort of use of these huge digital image gatherings donates to their admiration. However, not all of these folders deliver relevant indexing information. From the outcomes, it is difficult to discover data that the user can be absorbed in. Therefore, in order to determine the significance of the data, it is important to identify the contents in an informative manner. Image annotation can be one of the greatest problematic domains in multimedia research and computer vision.… More >

  • Open Access

    ARTICLE

    Enhanced Detection of Cerebral Atherosclerosis Using Hybrid Algorithm of Image Segmentation

    Shakunthala Masi*, Helenprabha Kuttiappan

    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 733-744, 2023, DOI:10.32604/iasc.2023.025919

    Abstract In medical science for envisaging human body’s phenomenal structure a major part has been driven by image processing techniques. Major objective of this work is to detect of cerebral atherosclerosis for image segmentation application. Detection of some abnormal structures in human body has become a difficult task to complete with some simple images. For expounding and distinguishing neural architecture of human brain in an effective manner, MRI (Magnetic Resonance Imaging) is one of the most suitable and significant technique. Here we work on detection of Cerebral Atherosclerosis from MRI images of patients. Cerebral Atherosclerosis is… More >

  • Open Access

    ARTICLE

    Automated Brain Tumor Diagnosis Using Deep Residual U-Net Segmentation Model

    R. Poonguzhali1, Sultan Ahmad2, P. Thiruvannamalai Sivasankar3, S. Anantha Babu3, Pranav Joshi4, Gyanendra Prasad Joshi5, Sung Won Kim6,*

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 2179-2194, 2023, DOI:10.32604/cmc.2023.032816

    Abstract Automated segmentation and classification of biomedical images act as a vital part of the diagnosis of brain tumors (BT). A primary tumor brain analysis suggests a quicker response from treatment that utilizes for improving patient survival rate. The location and classification of BTs from huge medicinal images database, obtained from routine medical tasks with manual processes are a higher cost together in effort and time. An automatic recognition, place, and classifier process was desired and useful. This study introduces an Automated Deep Residual U-Net Segmentation with Classification model (ADRU-SCM) for Brain Tumor Diagnosis. The presented… More >

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