Chee Chin Lim^1,2,*, Hui Wen Tiu¹, Qi Wei Oung^1,3, Chiew Chea Lau⁴, Xiao Jian Tan^2,5

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.070468 - 12 January 2026

Abstract Breast cancer remains one of the leading causes of cancer mortality world-wide, with accurate molecular subtyping is critical for guiding treatment and improving patient outcomes. Traditional molecular subtyping via immuno-histochemistry (IHC) test is invasive, time-consuming, and may not fully represent tumor heterogeneity. This study proposes a non-invasive approach using digital mammography images and deep learning algorithm for classifying breast cancer molecular subtypes. Four pretrained models, including two Convolutional Neural Networks (MobileNet_V3_Large and VGG-16) and two Vision Transformers (ViT_B_16 and ViT_Base_Patch16_Clip_224) were fine-tuned to classify images into HER2-enriched, Luminal, Normal-like, and Triple Negative subtypes. Hyperparameter tuning,… 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 >

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 >

Şeref Barbaros Arik^1,2,*, İnanç Güvenç^1,2

Canadian Journal of Urology, Vol.32, No.6, pp. 569-578, 2025, DOI:10.32604/cju.2025.068094 - 30 December 2025

Abstract Objectives: Differentiating benign from malignant testicular lesions is essential to avoid unnecessary surgery and ensure timely intervention. While conventional ultrasound remains the first-line imaging method, elastography and MRI provide additional functional and structural information. This study assesses the diagnostic utility of testicular elastography and magnetic resonance imaging (MRI) in differentiating benign and malignant testicular lesions. Methods: Patients with sonographically detected testicular masses were retrospectively evaluated using elastography, scrotal MRI, and tumor markers. Quantitative and qualitative imaging findings, lesion size, and laboratory values were recorded. Statistical analyses included Fisher’s exact test, logistic regression, Receiver operating characteristic… More >

Roland Mangold, Timea Teglas^*

BIOCELL, Vol.49, No.12, pp. 2239-2244, 2025, DOI:10.32604/biocell.2025.069925 - 24 December 2025

Abstract The quality of life in older adulthood is greatly influenced by cognitive aging, which in turn is affected by nutrition, especially as it relates to hippocampal function. Although the link between hippocampal function and nutrition is defined, the exact mechanics are still unknown. The commentary addresses how docosahexaenoic acid (DHA) contributes to age-related cognitive decline and may play a role in promoting neurogenesis and neuroplasticity on the molecular level. The current challenge to our understanding is to investigate how DHA influences hippocampal function and cognitive aging, which would be possible and even more detailed with More >

Maryam^1,*, Rahayu Puji², Luthfi Muhammad Zulfikar², Ikhsandy Ferry², Nadiyah Khairun¹, Hidayat³, Ilyas Rushdan Ahmad⁴, Syafri Edi⁵

Journal of Renewable Materials, Vol.13, No.12, pp. 2431-2451, 2025, DOI:10.32604/jrm.2025.02024-0063 - 23 December 2025

Abstract The development of the bioplastics industry addresses critical issues such as environmental pollution and food safety concerns. However, the industrialization of bioplastics remains underdeveloped due to challenges such as high production costs and suboptimal material characteristics. To enhance these characteristics, this study investigates bioplastics reinforced with Nanocrystalline Cellulose (NCC) derived from Oil Palm Empty Fruit Bunches (OPEFB), incorporating dispersing agents. The research employs a Central Composite Design from the Response Surface Methodology (RSM) with two factors: the type of dispersing agent (KCl and NaCl) and the NCC concentration from OPEFB (1%–5%), along with the dispersing… 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 >

Damodharan Palaniappan¹, Tan Kuan Tak², K. Vijayan³, Balajee Maram⁴, Pravin R Kshirsagar⁵, Naim Ahmad^6,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 4107-4127, 2025, DOI:10.32604/cmes.2025.073387 - 23 December 2025

Abstract A phase-aware cross-modal framework is presented that synthesizes UWF_FA from non-invasive UWF_RI for diabetic retinopathy (DR) stratification. A curated cohort of 1198 patients (2915 UWF_RI and 17,854 UWF_FA images) with strict registration quality supports training across three angiographic phases (initial, mid, final). The generator is based on a modified pix2pixHD with an added Gradient Variance Loss to better preserve microvasculature, and is evaluated using MAE, PSNR, SSIM, and MS-SSIM on held-out pairs. Quantitatively, the mid phase achieves the lowest MAE (98.76 ± 42.67), while SSIM remains high across phases. Expert review shows substantial agreement (Cohen’s More >

Asma Batool¹, Fahad Ahmed¹, Naila Sammar Naz¹, Ayman Altameem², Ateeq Ur Rehman^3,4, Khan Muhammad Adnan^5,*, Ahmad Almogren^6,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 4129-4152, 2025, DOI:10.32604/cmes.2025.073149 - 23 December 2025

Abstract Early and accurate cancer diagnosis through medical imaging is crucial for guiding treatment and enhancing patient survival. However, many state-of-the-art deep learning (DL) methods remain opaque and lack clinical interpretability. This paper presents an explainable artificial intelligence (XAI) framework that combines a fine-tuned Visual Geometry Group 16-layer network (VGG16) convolutional neural network with layer-wise relevance propagation (LRP) to deliver high-performance classification and transparent decision support. This approach is evaluated on the publicly available Kaggle kidney cancer imaging dataset, which comprises labeled cancerous and non-cancerous kidney scans. The proposed model achieved 98.75% overall accuracy, with precision, More >

Sarfaraz Abdul Sattar Natha^1,*, Mohammad Siraj^2,*, Majid Altamimi², Adamali Shah², Maqsood Mahmud³

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 4179-4201, 2025, DOI:10.32604/cmes.2025.072529 - 23 December 2025

Abstract A brain tumor is a disease in which abnormal cells form a tumor in the brain. They are rare and can take many forms, making them difficult to treat, and the survival rate of affected patients is low. Magnetic resonance imaging (MRI) is a crucial tool for diagnosing and localizing brain tumors. However, the manual interpretation of MRI images is tedious and prone to error. As artificial intelligence advances rapidly, DL techniques are increasingly used in medical imaging to accurately detect and diagnose brain tumors. In this study, we introduce a deep convolutional neural network… More >