Pengwei Guo¹, Xiao Tan^2,3,*, Yiming Liu⁴

Structural Durability & Health Monitoring, Vol.20, No.1, 2026, DOI:10.32604/sdhm.2025.071317 - 08 January 2026

Abstract Crack detection accuracy in computer vision is often constrained by limited annotated datasets. Although Generative Adversarial Networks (GANs) have been applied for data augmentation, they frequently introduce blurs and artifacts. To address this challenge, this study leverages Denoising Diffusion Probabilistic Models (DDPMs) to generate high-quality synthetic crack images, enriching the training set with diverse and structurally consistent samples that enhance the crack segmentation. The proposed framework involves a two-stage pipeline: first, DDPMs are used to synthesize high-fidelity crack images that capture fine structural details. Second, these generated samples are combined with real data to train… More >

Mohammed Debakla^1,*, Ali Mezaghrani¹, Khalifa Djemal², Imane Zouaneb¹

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-21, 2026, DOI:10.32604/cmc.2025.071927 - 09 December 2025

Abstract Magnetic Resonance Imaging (MRI) has a pivotal role in medical image analysis, for its ability in supporting disease detection and diagnosis. Fuzzy C-Means (FCM) clustering is widely used for MRI segmentation due to its ability to handle image uncertainty. However, the latter still has countless limitations, including sensitivity to initialization, susceptibility to local optima, and high computational cost. To address these limitations, this study integrates Grey Wolf Optimization (GWO) with FCM to enhance cluster center selection, improving segmentation accuracy and robustness. Moreover, to further refine optimization, Fuzzy Entropy Clustering was utilized for its distinctive features… More >

Bin Zhang^*, Zhancheng Xu

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-12, 2026, DOI:10.32604/cmc.2025.069979 - 09 December 2025

Abstract This study aimed to enhance the performance of semantic segmentation for autonomous driving by improving the 2DPASS model. Two novel improvements were proposed and implemented in this paper: dynamically adjusting the loss function ratio and integrating an attention mechanism (CBAM). First, the loss function weights were adjusted dynamically. The grid search method is used for deciding the best ratio of 7:3. It gives greater emphasis to the cross-entropy loss, which resulted in better segmentation performance. Second, CBAM was applied at different layers of the 2D encoder. Heatmap analysis revealed that introducing it after the second… More >

Cyreneo Dofitas¹, Yong-Woon Kim², Yung-Cheol Byun^3,*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-19, 2026, DOI:10.32604/cmc.2025.069374 - 09 December 2025

Abstract Recent advances in deep learning have significantly improved flood detection and segmentation from aerial and satellite imagery. However, conventional convolutional neural networks (CNNs) often struggle in complex flood scenarios involving reflections, occlusions, or indistinct boundaries due to limited contextual modeling. To address these challenges, we propose a hybrid flood segmentation framework that integrates a Vision Transformer (ViT) encoder with a U-Net decoder, enhanced by a novel Flood-Aware Refinement Block (FARB). The FARB module improves boundary delineation and suppresses noise by combining residual smoothing with spatial-channel attention mechanisms. We evaluate our model on a UAV-acquired flood More >

Seyong Jin¹, Muhammad Fayaz², L. Minh Dang³, Hyoung-Kyu Song³, Hyeonjoon Moon^2,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-23, 2026, DOI:10.32604/cmc.2025.070667 - 10 November 2025

Abstract Brain tumors require precise segmentation for diagnosis and treatment plans due to their complex morphology and heterogeneous characteristics. While MRI-based automatic brain tumor segmentation technology reduces the burden on medical staff and provides quantitative information, existing methodologies and recent models still struggle to accurately capture and classify the fine boundaries and diverse morphologies of tumors. In order to address these challenges and maximize the performance of brain tumor segmentation, this research introduces a novel SwinUNETR-based model by integrating a new decoder block, the Hierarchical Channel-wise Attention Decoder (HCAD), into a powerful SwinUNETR encoder. The HCAD… More >

Ameer Hamza, Robertas Damaševičius^*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-41, 2026, DOI:10.32604/cmc.2025.069721 - 10 November 2025

Abstract This systematic review aims to comprehensively examine and compare deep learning methods for brain tumor segmentation and classification using MRI and other imaging modalities, focusing on recent trends from 2022 to 2025. The primary objective is to evaluate methodological advancements, model performance, dataset usage, and existing challenges in developing clinically robust AI systems. We included peer-reviewed journal articles and high-impact conference papers published between 2022 and 2025, written in English, that proposed or evaluated deep learning methods for brain tumor segmentation and/or classification. Excluded were non-open-access publications, books, and non-English articles. A structured search was… 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 >

Moneerah Alotaibi^*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-20, 2026, DOI:10.32604/cmc.2025.069195 - 10 November 2025

Abstract Advanced traffic monitoring systems encounter substantial challenges in vehicle detection and classification due to the limitations of conventional methods, which often demand extensive computational resources and struggle with diverse data acquisition techniques. This research presents a novel approach for vehicle classification and recognition in aerial image sequences, integrating multiple advanced techniques to enhance detection accuracy. The proposed model begins with preprocessing using Multiscale Retinex (MSR) to enhance image quality, followed by Expectation-Maximization (EM) Segmentation for precise foreground object identification. Vehicle detection is performed using the state-of-the-art YOLOv10 framework, while feature extraction incorporates Maximally Stable Extremal… More >

Yanting Zhang¹, Qiyue Liu^1,2, Chuanzhao Tian^1,2,*, Xuewen Li¹, Na Yang¹, Feng Zhang¹, Hongyue Zhang³

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-25, 2026, DOI:10.32604/cmc.2025.068403 - 10 November 2025

Abstract High-resolution remote sensing images (HRSIs) are now an essential data source for gathering surface information due to advancements in remote sensing data capture technologies. However, their significant scale changes and wealth of spatial details pose challenges for semantic segmentation. While convolutional neural networks (CNNs) excel at capturing local features, they are limited in modeling long-range dependencies. Conversely, transformers utilize multihead self-attention to integrate global context effectively, but this approach often incurs a high computational cost. This paper proposes a global-local multiscale context network (GLMCNet) to extract both global and local multiscale contextual information from HRSIs.… 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 >