Jia Yan Lim^1,2, Siti Madiha Muhammad Amir³, Roslan Yahya³, Marta Peña Fernández², Tze Chuen Yap^1,*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-19, 2026, DOI:10.32604/cmc.2025.070707 - 09 December 2025

Abstract Additive Manufacturing, also known as 3D printing, has transformed conventional manufacturing by building objects layer by layer, with material extrusion or fused deposition modeling standing out as particularly popular. However, due to its manufacturing process and thermal nature, internal voids and pores are formed within the thermoplastic materials being fabricated, potentially leading to a decrease in mechanical properties. This paper discussed the effect of printing parameters on the porosity and the mechanical properties of the 3D printed polylactic acid (PLA) through micro-computed tomography (microCT), computational image analysis, and Charpy impact testing. The results for both… More >

Guangyu Xu^1,2, Siyuan Xu³, Siyu Lu^4,*, Yuxin Liu¹, Bo Yang¹, Junmin Lyu⁵, Wenfeng Zheng^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 4037-4053, 2025, DOI:10.32604/cmes.2025.074901 - 23 December 2025

Abstract Robust stereo disparity estimation plays a critical role in minimally invasive surgery, where dynamic soft tissues, specular reflections, and data scarcity pose major challenges to traditional end-to-end deep learning and deformable model-based methods. In this paper, we propose a novel disparity estimation framework that leverages a pretrained StyleGAN generator to represent the disparity manifold of Minimally Invasive Surgery (MIS) scenes and reformulates the stereo matching task as a latent-space optimization problem. Specifically, given a stereo pair, we search for the optimal latent vector in the intermediate latent space of StyleGAN, such that the photometric reconstruction… More >

Dawa Chyophel Lepcha^1,*, Bhawna Goyal^2,3, Ayush Dogra⁴, Ahmed Alkhayyat⁵, Prabhat Kumar Sahu⁶, Aaliya Ali⁷, Vinay Kukreja⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 1487-1573, 2025, DOI:10.32604/cmes.2025.070964 - 26 November 2025

Abstract Medical image analysis has become a cornerstone of modern healthcare, driven by the exponential growth of data from imaging modalities such as MRI, CT, PET, ultrasound, and X-ray. Traditional machine learning methods have made early contributions; however, recent advancements in deep learning (DL) have revolutionized the field, offering state-of-the-art performance in image classification, segmentation, detection, fusion, registration, and enhancement. This comprehensive review presents an in-depth analysis of deep learning methodologies applied across medical image analysis tasks, highlighting both foundational models and recent innovations. The article begins by introducing conventional techniques and their limitations, setting the… More >

Neethu Rose Thomas^1,2, J. Anitha², Cristina Popirlan³, Claudiu-Ionut Popirlan³, D. Jude Hemanth^2,*

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4407-4440, 2025, DOI:10.32604/cmc.2025.070689 - 23 October 2025

Abstract Integration of artificial intelligence in image processing methods has significantly improved the accuracy of the medical diagnostics pathway for early detection and analysis of kidney tumors. Computer-assisted image analysis can be an effective tool for early diagnosis of soft tissue tumors located remotely or in inaccessible anatomical locations. In this review, we discuss computer-based image processing methods using deep learning, convolutional neural networks (CNNs), radiomics, and transformer-based methods for kidney tumors. These techniques hold significant potential for automated segmentation, classification, and prognostic estimation with high accuracy, enabling more precise and personalized treatment planning. Special focus More >

Arham Adnan¹, Muhammad Tuaha Rizwan¹, Hafiz Muhammad Attaullah^1,2,*, Shakila Basheer³, Mohammad Tabrez Quasim⁴

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5073-5091, 2025, DOI:10.32604/cmc.2025.068797 - 23 October 2025

Abstract Artificial intelligence (AI), particularly deep learning algorithms utilizing convolutional neural networks, plays an increasingly pivotal role in enhancing medical image examination. It demonstrates the potential for improving diagnostic accuracy within dental care. Orthopantomograms (OPGs) are essential in dentistry; however, their manual interpretation is often inconsistent and tedious. To the best of our knowledge, this is the first comprehensive application of YOLOv5m for the simultaneous detection and classification of six distinct dental pathologies using panoramic OPG images. The model was trained and refined on a custom dataset that began with 232 panoramic radiographs and was later… More >

Sanaa Lakrouni^*, Slimane Bah, Marouane Sebgui

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5555-5569, 2025, DOI:10.32604/cmc.2025.067654 - 23 October 2025

Abstract Federated Learning (FL) has recently emerged as a promising paradigm that enables medical institutions to collaboratively train robust models without centralizing sensitive patient information. Data collected from different institutions represent distinct source domains. Consequently, discrepancies in feature distributions can significantly hinder a model’s generalization to unseen domains. While domain generalization (DG) methods have been proposed to address this challenge, many may compromise data privacy in FL by requiring clients to transmit their local feature representations to the server. Furthermore, existing adversarial training methods, commonly used to align marginal feature distributions, fail to ensure the consistency… More >

Osama M. Alshehri¹, Ahmad Shaf^2,*, Muhammad Irfan^3,*, Mohammed M. Jalal⁴, Malik A. Altayar⁴, Mohammed H. Abu-Alghayth⁵, Humood Al Shmrany⁶, Tariq Ali⁷, Toufique A. Soomro⁸, Ali G. Alkhathami⁹

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 1165-1196, 2025, DOI:10.32604/cmes.2025.067150 - 31 July 2025

Abstract Background: Accurate classification of normal blood cells is a critical foundation for automated hematological analysis, including the detection of pathological conditions like leukemia. While convolutional neural networks (CNNs) excel in local feature extraction, their ability to capture global contextual relationships in complex cellular morphologies is limited. This study introduces a hybrid CNN-Transformer framework to enhance normal blood cell classification, laying the groundwork for future leukemia diagnostics. Methods: The proposed architecture integrates pre-trained CNNs (ResNet50, EfficientNetB3, InceptionV3, CustomCNN) with Vision Transformer (ViT) layers to combine local and global feature modeling. Four hybrid models were evaluated on… More >

Yao-Tien Chen¹, Nisar Ahmad^1,*, Khursheed Aurangzeb²

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 1197-1224, 2025, DOI:10.32604/cmes.2025.066580 - 31 July 2025

Abstract Accurate and efficient brain tumor segmentation is essential for early diagnosis, treatment planning, and clinical decision-making. However, the complex structure of brain anatomy and the heterogeneous nature of tumors present significant challenges for precise anomaly detection. While U-Net-based architectures have demonstrated strong performance in medical image segmentation, there remains room for improvement in feature extraction and localization accuracy. In this study, we propose a novel hybrid model designed to enhance 3D brain tumor segmentation. The architecture incorporates a 3D ResNet encoder known for mitigating the vanishing gradient problem and a 3D U-Net decoder. Additionally, to… More > Graphic Abstract

Sameera V Mohd Sagheer^1,*, Meghana K H², P M Ameer³, Muneer Parayangat⁴, Mohamed Abbas⁴

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4259-4297, 2025, DOI:10.32604/cmc.2025.063726 - 30 July 2025

Abstract Integrating multiple medical imaging techniques, including Magnetic Resonance Imaging (MRI), Computed Tomography, Positron Emission Tomography (PET), and ultrasound, provides a comprehensive view of the patient health status. Each of these methods contributes unique diagnostic insights, enhancing the overall assessment of patient condition. Nevertheless, the amalgamation of data from multiple modalities presents difficulties due to disparities in resolution, data collection methods, and noise levels. While traditional models like Convolutional Neural Networks (CNNs) excel in single-modality tasks, they struggle to handle multi-modal complexities, lacking the capacity to model global relationships. This research presents a novel approach for… More >

Vitor F. C. Sousa¹, Jorge Gil¹, Tiago E. F. Silva¹, Abílio M. P. de Jesus^1,2, Francisco J. G. Silva^1,3, João Manuel R. S. Tavares^1,2,*

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 177-199, 2025, DOI:10.32604/cmc.2025.062133 - 26 March 2025

Abstract The machining process remains relevant for manufacturing high-quality and high-precision parts, which can be found in industries such as aerospace and aeronautical, with many produced by turning, drilling, and milling processes. Monitoring and analyzing tool wear during these processes is crucial to assess the tool’s life and optimize the tool’s performance under study; as such, standards detail procedures to measure and assess tool wear for various tools. Measuring wear in machining tools can be time-consuming, as the process is usually manual, requiring human interaction and judgment. In the present work, an automated offline flank wear… More >