Mohd Annuar Isa¹, Mohamad Nur Khairul Hafizi Rohani^1,*, Baharuddin Ismail¹, Mohamad Kamarol Jamil¹, Muzamir Isa¹, Afifah Shuhada Rosmi¹, Mohd Aminudin Jamlos², Wan Azani Mustafa¹, Nurulbariah Idris³, Abdullahi Abubakar Mas’ud⁴

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 741-760, 2023, DOI:10.32604/cmc.2023.036077

Abstract Electrical trees are an aging mechanism most associated with partial discharge (PD) activities in crosslinked polyethylene (XLPE) insulation of high-voltage (HV) cables. Characterization of electrical tree structures gained considerable attention from researchers since a deep understanding of the tree morphology is required to develop new insulation material. Two-dimensional (2D) optical microscopy is primarily used to examine tree structures and propagation shapes with image segmentation methods. However, since electrical trees can emerge in different shapes such as bush-type or branch-type, treeing images are complicated to segment due to manifestation of convoluted tree branches, leading to a… More >

Ali F. Khalifa¹, Eman Badr^1,2,*

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 >

Ahmed A. Alsheikhy^1,*, 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 >

Sanjiban Sekhar Roy¹, Ching-Hsien Hsu^2,3,4,*, 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 >

Hande Mao^1,2, Hongzhe Yan¹, Lei Lin¹, Wentao Dong^1,3, Yuhang Li¹, Yuliang Liu^2,4,*, 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 >

Fahad Alraddady¹, E. A. Zanaty², Aida H. Abu bakr³, Walaa M. Abd-Elhafiez^4,5,*

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 >

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 >

Jun Liu¹, Geng Yuan², Changdi Yang², Houbing Song³, Liang Luo^4,*

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 >

R. Jayaraj^1,*, 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 >

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 >