Dongsheng Li, Chunsheng Li^*, Kejia Zhang, Tao Liu, Fang Liu, Jingsong Yin, Mingyue Liao

Computer Systems Science and Engineering, Vol.47, No.1, pp. 1209-1231, 2023, DOI:10.32604/csse.2023.034395

Abstract Intelligent identification of sandstone slice images using deep learning technology is the development trend of mineral identification, and accurate mineral particle segmentation is the most critical step for intelligent identification. A typical identification model requires many training samples to learn as many distinguishable features as possible. However, limited by the difficulty of data acquisition, the high cost of labeling, and privacy protection, this has led to a sparse sample number and cannot meet the training requirements of deep learning image identification models. In order to increase the number of samples and improve the training effect of deep learning models, this… More >

Muwei Jian^1,2,#,*, Ronghua Wu^1,#, Hongyu Chen¹, Lanqi Fu³, Chengdong Yang¹

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 705-716, 2023, DOI:10.32604/cmes.2023.027425

Abstract In intelligent perception and diagnosis of medical equipment, the visual and morphological changes in retinal vessels are closely related to the severity of cardiovascular diseases (e.g., diabetes and hypertension). Intelligent auxiliary diagnosis of these diseases depends on the accuracy of the retinal vascular segmentation results. To address this challenge, we design a Dual-Branch-UNet framework, which comprises a Dual-Branch encoder structure for feature extraction based on the traditional U-Net model for medical image segmentation. To be more explicit, we utilize a novel parallel encoder made up of various convolutional modules to enhance the encoder portion of the original U-Net. Then, image… More >

Mohamed Ibrahim Waly^*

Computer Systems Science and Engineering, Vol.46, No.3, pp. 3159-3174, 2023, DOI:10.32604/csse.2023.035900

Abstract Recently, computer assisted diagnosis (CAD) model creation has become more dependent on medical picture categorization. It is often used to identify several conditions, including brain disorders, diabetic retinopathy, and skin cancer. Most traditional CAD methods relied on textures, colours, and forms. Because many models are issue-oriented, they need a more substantial capacity to generalize and cannot capture high-level problem domain notions. Recent deep learning (DL) models have been published, providing a practical way to develop models specifically for classifying input medical pictures. This paper offers an intelligent beetle antenna search (IBAS-DTL) method for classifying medical images facilitated by deep transfer… More >

Jiaqi Shao^1,#, Shuwen Chen^1,2,3,#,*, Jin Zhou^1,#, Huisheng Zhu¹, Ziyi Wang¹, Mackenzie Brown^4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.3, pp. 2173-2219, 2023, DOI:10.32604/cmes.2023.025499

Abstract As a mainstream research direction in the field of image segmentation, medical image segmentation plays a key role in the quantification of lesions, three-dimensional reconstruction, region of interest extraction and so on. Compared with natural images, medical images have a variety of modes. Besides, the emphasis of information which is conveyed by images of different modes is quite different. Because it is time-consuming and inefficient to manually segment medical images only by professional and experienced doctors. Therefore, large quantities of automated medical image segmentation methods have been developed. However, until now, researchers have not developed a universal method for all… More >

Sungjin Lee¹, Ahyoung Lee², Min Hong^3,*

Computer Systems Science and Engineering, Vol.46, No.2, pp. 2543-2554, 2023, DOI:10.32604/csse.2023.037055

Abstract Specific medical data has limitations in that there are not many numbers and it is not standardized. to solve these limitations, it is necessary to study how to efficiently process these limited amounts of data. In this paper, deep learning methods for automatically determining cardiovascular diseases are described, and an effective preprocessing method for CT images that can be applied to improve the performance of deep learning was conducted. The cardiac CT images include several parts of the body such as the heart, lungs, spine, and ribs. The preprocessing step proposed in this paper divided CT image data into regions… More >

Ahmed A. Alsheikhy^1,*, Ahmad S. Azzahrani¹, A. Khuzaim Alzahrani², Tawfeeq Shawly³

Computer Systems Science and Engineering, Vol.46, No.2, pp. 2021-2037, 2023, DOI:10.32604/csse.2023.036107

Abstract A brain tumor is an excessive development of abnormal and uncontrolled cells in the brain. This growth is considered deadly since it may cause death. The brain controls numerous functions, such as memory, vision, and emotions. Due to the location, size, and shape of these tumors, their detection is a challenging and complex task. Several efforts have been conducted toward improved detection and yielded promising results and outcomes. However, the accuracy should be higher than what has been reached. This paper presents a method to detect brain tumors with high accuracy. The method works using an image segmentation technique and… More >

P. M. Siva Raja^1,*, S. Brinthakumari², K. Ramanan³

Computer Systems Science and Engineering, Vol.46, No.2, pp. 2589-2599, 2023, DOI:10.32604/csse.2023.028542

Abstract Brain tumor detection and division is a difficult tedious undertaking in clinical image preparation. When it comes to the new technology that enables accurate identification of the mysterious tissues of the brain, magnetic resonance imaging (MRI) is a great tool. It is possible to alter the tumor’s size and shape at any time for any number of patients by using the Brain picture. Radiologists have a difficult time sorting and classifying tumors from multiple images. Brain tumors may be accurately detected using a new approach called Nonlinear Teager-Kaiser Iterative Infomax Boost Clustering-Based Image Segmentation (NTKFIBC-IS). Teager-Kaiser filtering is used to… More >

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 high misclassification rate during segmentation.… 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 described, and various models 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 a Convolutional Neural Network (CNN)… More >