Wasfieh Nazzal¹, Ezequiel López-Rubio^1,2,3, Miguel A. Molina-Cabello^1,2,3, Karl Thurnhofer-Hemsi^1,2,3,*

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.077561 - 12 March 2026

Abstract Automatic and accurate medical image segmentation remains a fundamental task in computer-aided diagnosis and treatment planning. Recent advances in foundation models, such as the medical-focused Segment Anything Model (MedSAM), have demonstrated strong performance but face challenges in many medical applications due to anatomical complexity and a limited domain-specific prompt. This work introduces a methodology that enhances segmentation robustness and precision by automatically generating multiple informative point prompts, rather than relying on single inputs. The proposed approach randomly samples sets of spatially distributed point prompts based on image features, enabling MedSAM to better capture fine-grained anatomical… More >

Jeng-Shyang Pan^1,2, Yan-Na Wei³, Ling-Da Chi⁴, Shu-Chuan Chu^1,*, Ru-Yu Wang⁵, Junzo Watada⁶

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2025.074447 - 12 March 2026

Abstract Multilevel image segmentation is a critical task in image analysis, which imposes high requirements on the global search capability and convergence efficiency of segmentation algorithms. In this paper, an improved Artificial Protozoa Optimization algorithm, termed the two-stage Taguchi-assisted Gaussian–Lévy Artificial Protozoa Optimization (TGAPO) algorithm, is proposed and applied to multilevel image segmentation. The proposed algorithm adopts a two-stage evolutionary mechanism. In the first stage, Gaussian perturbation is introduced to enhance local search capability; in the second stage, Lévy flight is incorporated to expand the global search range; and finally, the Taguchi strategy is employed to… More >

Tianze Yu, Jianxun Zhang^*, Hongji Chen

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2026.073119 - 10 February 2026

Abstract Camouflaged Object Detection (COD) aims to identify objects that share highly similar patterns—such as texture, intensity, and color—with their surrounding environment. Due to their intrinsic resemblance to the background, camouflaged objects often exhibit vague boundaries and varying scales, making it challenging to accurately locate targets and delineate their indistinct edges. To address this, we propose a novel camouflaged object detection network called Edge-Guided and Multi-scale Fusion Network (EGMFNet), which leverages edge-guided multi-scale integration for enhanced performance. The model incorporates two innovative components: a Multi-scale Fusion Module (MSFM) and an Edge-Guided Attention Module (EGA). These designs… More >

Nouf Abdullah Almujally¹, Tanvir Fatima Naik Bukht², Shuaa S. Alharbi³, Asaad Algarni⁴, Ahmad Jalal^2,5, Jeongmin Park^6,*

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.072243 - 10 February 2026

Abstract Recognising human-object interactions (HOI) is a challenging task for traditional machine learning models, including convolutional neural networks (CNNs). Existing models show limited transferability across complex datasets such as D3D-HOI and SYSU 3D HOI. The conventional architecture of CNNs restricts their ability to handle HOI scenarios with high complexity. HOI recognition requires improved feature extraction methods to overcome the current limitations in accuracy and scalability. This work proposes a Novel quantum gate-enabled hybrid CNN (QEH-CNN) for effective HOI recognition. The model enhances CNN performance by integrating quantum computing components. The framework begins with bilateral image filtering,… More >

Pham Huy Thong¹, Hoang Thi Canh^2,3,*, Nguyen Tuan Huy⁴, Nguyen Long Giang^1,*, Luong Thi Hong Lan⁴

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.071776 - 12 January 2026

Abstract Satellite image segmentation plays a crucial role in remote sensing, supporting applications such as environmental monitoring, land use analysis, and disaster management. However, traditional segmentation methods often rely on large amounts of labeled data, which are costly and time-consuming to obtain, especially in large-scale or dynamic environments. To address this challenge, we propose the Semi-Supervised Multi-View Picture Fuzzy Clustering (SS-MPFC) algorithm, which improves segmentation accuracy and robustness, particularly in complex and uncertain remote sensing scenarios. SS-MPFC unifies three paradigms: semi-supervised learning, multi-view clustering, and picture fuzzy set theory. This integration allows the model to effectively… More >

Bo Li, Jie Jia^*, Peiwen Tan, Xinyan Chen, Dongjin Li

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.071186 - 12 January 2026

Abstract Medical image segmentation is of critical importance in the domain of contemporary medical imaging. However, U-Net and its variants exhibit limitations in capturing complex nonlinear patterns and global contextual information. Although the subsequent U-KAN model enhances nonlinear representation capabilities, it still faces challenges such as gradient vanishing during deep network training and spatial detail loss during feature downsampling, resulting in insufficient segmentation accuracy for edge structures and minute lesions. To address these challenges, this paper proposes the RE-UKAN model, which innovatively improves upon U-KAN. Firstly, a residual network is introduced into the encoder to effectively… More >

Arvind Mukundan^1,2, Riya Karmakar¹, Devansh Gupta³, Hsiang-Chen Wang^1,4,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-23, 2026, DOI:10.32604/cmc.2025.069646 - 10 November 2025

Abstract Modern manufacturing processes have become more reliant on automation because of the accelerated transition from Industry 3.0 to Industry 4.0. Manual inspection of products on assembly lines remains inefficient, prone to errors and lacks consistency, emphasizing the need for a reliable and automated inspection system. Leveraging both object detection and image segmentation approaches, this research proposes a vision-based solution for the detection of various kinds of tools in the toolkit using deep learning (DL) models. Two Intel RealSense D455f depth cameras were arranged in a top down configuration to capture both RGB and depth images… More >

Angelin Gladston¹, Swedha Velraj^2,*, Harini Nadarajan¹

Congenital Heart Disease, Vol.20, No.6, pp. 769-792, 2025, DOI:10.32604/chd.2025.068879 - 10 February 2026

Abstract Background: Coronary artery disease (CAD) is a major global health concern requiring efficient and accurate diagnostic methods. Manual interpretation of coronary computed tomography angiography (CTA) images is time-consuming and prone to interobserver variability, underscoring the need for automated segmentation and stenosis detection tools. Methods: This study presents a hybrid multi-scale 3D segmentation framework utilizing both 3D U-Net and Enhanced 3D U-Net architectures, designed to balance computational efficiency and anatomical precision. Processed CTA images from the ImageCAS dataset underwent data standardization, normalization, and augmentation. The framework applies ensemble learning to merge coarse and fine segmentation masks, followed… More >

Tanvir Fatima Naik Bukht^1,2, Yanfeng Wu¹, Nouf Abdullah Almujally³, Shuoa S. AItarbi⁴, Hameedur Rahman², Ahmad Jalal^2,5,*, Hui Liu^1,6,7,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 4013-4036, 2025, DOI:10.32604/cmes.2025.071850 - 23 December 2025

Abstract Human activity recognition (HAR) is crucial in fields like robotics, surveillance, and healthcare, enabling systems to understand and respond to human actions. Current models often struggle with complex datasets, making accurate recognition challenging. This study proposes a quantum-integrated Convolutional Neural Network (QI-CNN) to enhance HAR performance. The traditional models demonstrate weak performance in transferring learned knowledge between diverse complex data collections, including D3D-HOI and Sysu 3D HOI. HAR requires better extraction models and techniques that must address current challenges to achieve improved accuracy and scalability. The model aims to enhance HAR task performance by combining… More >

Batyrkhan Omarov^1,2,3,4,*

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2195-2253, 2025, DOI:10.32604/cmc.2025.064799 - 23 September 2025

Abstract Deep learning now underpins many state-of-the-art systems for biomedical image and signal processing, enabling automated lesion detection, physiological monitoring, and therapy planning with accuracy that rivals expert performance. This survey reviews the principal model families as convolutional, recurrent, generative, reinforcement, autoencoder, and transfer-learning approaches as emphasising how their architectural choices map to tasks such as segmentation, classification, reconstruction, and anomaly detection. A dedicated treatment of multimodal fusion networks shows how imaging features can be integrated with genomic profiles and clinical records to yield more robust, context-aware predictions. To support clinical adoption, we outline post-hoc explainability More >