Xiaoliang Lei¹, Xiaosheng Yu^2,*, Hao Wu³, Chengdong Wu^2,*, Jingsi Zhang²

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3797-3811, 2023, DOI:10.32604/cmc.2023.042069

Abstract Accurate tumor segmentation from brain tissues in Magnetic Resonance Imaging (MRI) imaging is crucial in the pre-surgical planning of brain tumor malignancy. MRI images’ heterogeneous intensity and fuzzy boundaries make brain tumor segmentation challenging. Furthermore, recent studies have yet to fully employ MRI sequences’ considerable and supplementary information, which offers critical a priori knowledge. This paper proposes a clinical knowledge-based hybrid Swin Transformer multimodal brain tumor segmentation algorithm based on how experts identify malignancies from MRI images. During the encoder phase, a dual backbone network with a Swin Transformer backbone to capture long dependencies from 3D MR images and a… More >

Asifa Mehmood Qureshi¹, Nouf Abdullah Almujally², Saud S. Alotaibi³, Mohammed Hamad Alatiyyah⁴, Jeongmin Park^5,*

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3707-3725, 2023, DOI:10.32604/cmc.2023.040738

Abstract Road congestion, air pollution, and accident rates have all increased as a result of rising traffic density and worldwide population growth. Over the past ten years, the total number of automobiles has increased significantly over the world. In this paper, a novel method for intelligent traffic surveillance is presented. The proposed model is based on multilabel semantic segmentation using a random forest classifier which classifies the images into five classes. To improve the results, mean-shift clustering was applied to the segmented images. Afterward, the pixels given the label for the vehicle were extracted and blob detection was applied to mark… More >

Javed Rashid^1,2, Imran Khan¹, Irshad Ahmed Abbasi³, Muhammad Rizwan Saeed⁴, Mubbashar Saddique^5,*, Mohamed Abbas^6,7

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3897-3920, 2023, DOI:10.32604/cmc.2023.040356

Abstract Many plant species have a startling degree of morphological similarity, making it difficult to split and categorize them reliably. Unknown plant species can be challenging to classify and segment using deep learning. While using deep learning architectures has helped improve classification accuracy, the resulting models often need to be more flexible and require a large dataset to train. For the sake of taxonomy, this research proposes a hybrid method for categorizing guava, potato, and java plum leaves. Two new approaches are used to form the hybrid model suggested here. The guava, potato, and java plum plant species have been successfully… More >

Wenran Jia¹, Simin Ma¹, Peng Geng¹, Yan Sun^2,*

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3393-3411, 2023, DOI:10.32604/cmc.2023.040091

Abstract Retinal vessel segmentation in fundus images plays an essential role in the screening, diagnosis, and treatment of many diseases. The acquired fundus images generally have the following problems: uneven illumination, high noise, and complex structure. It makes vessel segmentation very challenging. Previous methods of retinal vascular segmentation mainly use convolutional neural networks on U Network (U-Net) models, and they have many limitations and shortcomings, such as the loss of microvascular details at the end of the vessels. We address the limitations of convolution by introducing the transformer into retinal vessel segmentation. Therefore, we propose a hybrid method for retinal vessel… More >

Kainat Nazir¹, Tahir Mustafa Madni¹, Uzair Iqbal Janjua¹, Umer Javed², Muhammad Attique Khan³, Usman Tariq⁴, Jae-Hyuk Cha^5,*

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 2861-2877, 2023, DOI:10.32604/cmc.2023.039181

Abstract Brain tumor significantly impacts the quality of life and changes everything for a patient and their loved ones. Diagnosing a brain tumor usually begins with magnetic resonance imaging (MRI). The manual brain tumor diagnosis from the MRO images always requires an expert radiologist. However, this process is time-consuming and costly. Therefore, a computerized technique is required for brain tumor detection in MRI images. Using the MRI, a novel mechanism of the three-dimensional (3D) Kronecker convolution feature pyramid (KCFP) is used to segment brain tumors, resolving the pixel loss and weak processing of multi-scale lesions. A single dilation rate was replaced… More >

Jingyao Liu^1,2, Qinghe Feng⁴, Jiashi Zhao^2,3, Yu Miao^2,3, Wei He², Weili Shi^2,3, Zhengang Jiang^2,3,*

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 2649-2665, 2023, DOI:10.32604/cmc.2023.038891

Abstract The coronavirus disease 2019 (COVID-19) has severely disrupted both human life and the health care system. Timely diagnosis and treatment have become increasingly important; however, the distribution and size of lesions vary widely among individuals, making it challenging to accurately diagnose the disease. This study proposed a deep-learning disease diagnosis model based on weakly supervised learning and clustering visualization (W_CVNet) that fused classification with segmentation. First, the data were preprocessed. An optimizable weakly supervised segmentation preprocessing method (O-WSSPM) was used to remove redundant data and solve the category imbalance problem. Second, a deep-learning fusion method was used for feature extraction… More >

Siyi Duan¹, Mingsheng Wei^1,*, Shidang Li^1,*, Weiqiang Tan², Bencheng Yu³

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 619-638, 2024, DOI:10.32604/cmes.2023.029572

Abstract This paper examines the performance of Full-Duplex Cooperative Rate Splitting (FD-CRS) with Simultaneous Wireless Information and Power Transfer (SWIPT) support in Multiple Input Single Output (MISO) networks. In a Rate Splitting Multiple Access (RSMA) multicast system with two local users and one remote user, the common data stream contains the needs of all users, and all users can decode the common data stream. Therefore, each user can receive some information that other users need, and local users with better channel conditions can use this information to further enhance the reception reliability and data rate of users with poor channel quality.… More >

Yuling Dou¹, Fengqin Yao¹, Xiandong Wang¹, Liang Qu², Long Chen³, Zhiwei Xu⁴, Laihui Ding⁴, Leon Bevan Bullock¹, Guoqiang Zhong¹, Shengke Wang^1,*

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 >

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 >

Sidong Wu, Cuiping Duan, Bufan Ren, Liuquan Ren, Tao Jiang, Jianying Yuan^*, Jiajia Liu, Dequan Guo

Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2707-2726, 2023, DOI:10.32604/iasc.2023.038948

Abstract The complexity of application scenarios and the enormous volume of point cloud data make it difficult to quickly and effectively segment the scenario only based on the point cloud. In this paper, to address the semantic segmentation for safety driving of unmanned shuttle buses, an accurate and effective point cloud-based semantic segmentation method is proposed for specified scenarios (such as campus). Firstly, we analyze the characteristic of the shuttle bus scenarios and propose to use ROI selection to reduce the total points in computation, and then propose an improved semantic segmentation model based on Cylinder3D, which improves mean Intersection over… More >