Hafeez Rahman¹, Naveed Butt¹, Naila Sammar Naz¹, Fahad Ahmed¹, Muhammad Saleem¹, Adnan Khan^2,3,4, Khan Muhammad Adnan^5,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.081316 - 27 April 2026

Abstract Gastrointestinal polyps are well-known precursors to colorectal cancer (CRC), making their accurate detection and segmentation during colonoscopy essential for early diagnosis and cancer prevention. Deep learning–based segmentation models trained on publicly available datasets such as Kvasir-SEG have demonstrated promising performance; however, two key challenges remain: limited robustness across diverse polyp morphologies and endoscopic imaging conditions, and the lack of interpretable decision-making mechanisms that support clinical trust and validation. Many existing centralized segmentation approaches are primarily optimized using overlap-based metrics such as the Dice coefficient and intersection over union (IoU), without adequately analyzing challenging cases such… More >

Mamoona Jabbar, Uzma Jamil^*, Muhammad Younas, Bushra Zafar

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.080241 - 27 April 2026

Abstract Magnetic Resonance Imaging is one of the most commonly used neuro-oncology imaging modalities, which is a non-invasive mode of imaging and helps in detecting brain abnormalities in an effective way. Earlier researchers have demonstrated that brain tumor segmentation and classification can be effectively performed using deep learning techniques. Existing studies are primarily aimed at increasing prediction accuracy and provide insignificant consideration to model interpretability, limiting their practical application in clinical practice. To address this limitation, this research presents a two-stage explainable deep learning model, which combines transformer-based segmentation with an ensemble classification model that is… More >

Rebika Rai^1,*, Totan Bharasa², Arunita Das³, Krishna Gopal Dhal³

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.079037 - 27 April 2026

Abstract Snake Optimizer (SO) is a popular optimization algorithm developed by Hashim and Hussien, based on the competitive and selective mating nature of snakes. By emulating such natural methods, SO presents an intelligent method to solve complicated optimization problems, making it a valuable tool in various scientific and technological applications. This paper provides an extensive review of the SO, its inception, the development of different variants, and applications. This paper identifies several SO variants, such as improved SO variants using different strategies, hybridized SO variants with other metaheuristics, Binary SO variants to solve discrete optimization problems,… More >

Ghada Atteia¹, Abdulaziz Altamimi², Nihal Abuzinadah³, Khaled Alnowaiser⁴, Muhammad Umer^5,*, Yunyoung Nam⁶, Yongwon Cho^6,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.078510 - 27 April 2026

Abstract Cardiovascular diseases (CVD) remain the leading cause of global mortality, making early and accurate diagnosis essential for improving patient outcomes. However, most existing deep learning approaches address cardiac image segmentation or disease classification independently, limiting their effectiveness in complex clinical decision-making scenarios. In this study, we propose an explainable spatio-temporal deep learning framework that integrates segmentation-guided representation learning with efficient temporal modeling for automated CVD detection. The proposed architecture incorporates the Segment Anything Model for Medical Imaging in 2D (SAM-Med2D) to achieve accurate cardiac structure segmentation, followed by Mamba-based temporal feature extraction and Transformer-driven spatial… More >

Oskar Kapuśniak¹, Adam Piórkowski^2,*, Julia Lasek³, Karolina Nurzyńska⁴

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076062 - 09 April 2026

Abstract The efficacy of Active Shape Models (ASM) for automated ventricular segmentation was evaluated to address the computational demands of manual segmentation and the interpretability limitations of deep learning. A statistical shape model was constructed using a limited cohort of 19 Coronary Computed Tomography Angiography (CCTA) scans derived from patients with diverse cardiac abnormalities. Principal Component Analysis (PCA) was employed to encapsulate morphological variability, and strict point correspondence was enforced to maintain topological consistency. Validation was conducted via leave-one-out cross-validation, benchmarking automated segmentations against expert-delineated ground truths using the Dice Similarity Coefficient (DSC) and Hausdorff Distance More >

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 >

Zhengyu Wu¹, Kejun Kang², Yixiu Liu^3,*, Chenpu Li³

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.075593 - 12 March 2026

Abstract Binary segmentation tasks in computer vision exhibit diverse appearance distributions and complex boundary characteristics. To address the limited generalization and adaptability of existing models across heterogeneous tasks, we propose Abel-Net, an Aggregated Bilateral Edge Localization Network designed as a universal framework for multi-task binary segmentation. Abel-Net integrates global and local contextual cues to enhance feature learning and edge precision. Specifically, a multi-scale feature pyramid fusion strategy is implemented via an Aggregated Skip Connection (ASC) module to strengthen feature adaptability, while the Edge Dual Localization (EDL) mechanism performs coarse-to-fine refinement through edge-aware supervision. Additionally, Edge Attention More >

Weimin Shi^1,*, Kuntao Lv¹, Chang Xuan¹, Ji Wu²

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2025.075203 - 12 March 2026

Abstract The application of deep learning in fabric defect detection has become increasingly widespread. To address false positives and false negatives in fabric roll seam detection, and to improve automation efficiency and product quality, we propose the Multi-scale Context DeepLabV3+ (MSC-DeepLabV3+), a semantic segmentation network designed for fabric roll seam detection, based on DeepLabV3+. The model improvements include enhancing the backbone performance through optimization of the UIB-MobileNetV2 network; designing the Dynamic Atrous and Sliding-window Fusion (DASF) module to improve adaptability to multi-scale seam structures with dynamic dilation rates and a sliding-window mechanism; and utilizing the Progressive… 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 >

Burhan Baraklı, Ahmet Küçüker^*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.2, 2026, DOI:10.32604/cmes.2026.077872 - 26 February 2026

Abstract Inspections of power transmission lines (PTLs) conducted using unmanned aerial vehicles (UAVs) are complicated by the fine structure of the lines and complex backgrounds, making accurate and efficient segmentation challenging. This study presents the Wavelet-Guided Transformer U-Net (WGT-UNet) model, a new hybrid network that combines Convolutional Neural Networks (CNNs), Discrete Wavelet Transform (DWT), and Transformer architectures. The model’s primary contribution is based on spatial and channel attention mechanisms derived from wavelet subbands to guide the Transformer’s self-attention structure. Thus, low and high frequency components are separated at each stage using DWT, suppressing structural noise and… More >