Yan-Hao Huang^*, Yu-Tse Huang

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076800 - 09 April 2026

Abstract Diabetic Retinopathy (DR) is a common microvascular complication of diabetes that progressively damages the retinal blood vessels and, without timely treatment, can lead to irreversible vision loss. In clinical practice, DR is typically diagnosed by ophthalmologists through visual inspection of fundus images, a process that is time-consuming and prone to inter- and intra-observer variability. Recent advances in artificial intelligence, particularly Convolutional Neural Networks (CNNs) and Transformer-based models, have shown strong potential for automated medical image classification and decision support. In this study, we propose a Position-Wise Attention-Enhanced Vision Transformer (PWAE-ViT), which integrates a positional attention… More >

Zhidong Wang, Yanping Lian^*, Mingjian Li, Jiawei Chen, Ruxin Gao

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.078312 - 30 March 2026

Abstract Metal additive manufacturing (AM) technology has promising applications across many fields due to its near-net-shape advantages. The quality of the as-built component is closely linked to the temperature evolution during the metal AM process, which exhibits strong nonlinearities, localized high gradients, and rapid cooling rates. Therefore, real-time prediction of the temperature field is essential for effective online process control to achieve high fabrication quality, which poses surprising challenges for numerical methods, as traditional methods suffer from the inherent time-consuming nature of fine time-space discretizations. In this study, we proposed an isothermal surface imaging and transfer… More >

Toa Saito¹, Kantawatchr Chaiprabha², Kosuke Takano¹, Gridsada Phanomchoeng², Ratchatin Chancharoen^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.077356 - 30 March 2026

Abstract This paper presents an automated imaging-to-CAD reconstruction system that combines telecentric vision and deep learning for high-accuracy digital reconstruction of printed circuit boards (PCBs). The framework integrates a telecentric camera with a Cartesian scanning platform to capture distortion-free, high-resolution PCB images, which are stitched into a single orthographic composite. A YOLO-based detection model, trained on a dataset of 270 PCB images across 23 component classes with data augmentation, identifies and localizes electronic components with a mean average precision of 0.932. Detected components are automatically matched to corresponding 3D CAD models from a part library and More >

Saira Ali Bhatti¹, Maqbool Khan^2,*, Arshad Ahmad³, Muhammad Shahid Anwar⁴, Leila Jamel⁵, Aisha M. Mashraqi⁶, Wadee Alhalabi^7,*

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.074688 - 30 March 2026

Abstract Medical imaging is essential in modern health care, allowing accurate diagnosis and effective treatment planning. These images, however, often demonstrate low contrast, noise, and brightness distortion that reduce their diagnostic reliability. This review presents a structured and comprehensive analysis of advanced histogram equalization (HE)-based techniques for medical image enhancement. Our review methodology encompasses: (1) classical HE approaches and related limitations in medical domains; (2) adaptive schemes like Adaptive Histogram Equalization (AHE) and Contrast Limited Adaptive Histogrma Equalization (CLAHE) and their advance variants; (3) brightness-preserving schemes like BBHE and MMBEBHE and related algorithms; (4) dynamic and More > Graphic Abstract

Abdullah Esmeray, Huseyin Burak Yazili^*, Mucahit Gelmis, Nazim Furkan Gunay, Caglar Dizdaroglu, Faruk Ozgor, Yasar Pazir, Ufuk Caglar

Canadian Journal of Urology, Vol.33, No.1, pp. 165-171, 2026, DOI:10.32604/cju.2025.069657 - 28 February 2026

Abstract Objectives: Supracostal access during percutaneous nephrolithotomy (PCNL) increases the risk of pulmonary complications. Although routine postoperative thoracic imaging is commonly performed to detect these events, its clinical necessity remains controversial. This study aimed to assess the necessity of routine postoperative thoracic imaging for detecting pulmonary complications in patients undergoing supracostal mini percutaneous nephrolithotomy (m-PCNL) surgery. Methods: A retrospective analysis was conducted on data from patients who underwent supracostal m-PCNL between 2017 and 2022 in a tertiary center. Excluding patients under 18, with kidney/skeletal anomalies, or active thoracic disease, 112 eligible patients were included. Patients were… More >

Marco Di Mitri^1,2,*, Edoardo Collautti^1,2, Cristian Bisanti³, Andrea Zulli¹, Alberto Mantovani¹, Annalisa Di Carmine³, Michelangelo Baldazzi⁴, Roberto Lo Piccolo¹, Riccardo Coletta^1,5, Lorenzo Masieri⁶, Mario Lima³

Canadian Journal of Urology, Vol.33, No.1, pp. 185-192, 2026, DOI:10.32604/cju.2025.067303 - 28 February 2026

Abstract Background: Double blind-ending ureter (DBU) is an extremely rare congenital anomaly involving a duplicated ureter with no connection to the renal pelvis or bladder, making diagnosis difficult. Case Description: A 10-year-old girl presented with recurrent abdominal pain and ultrasound evidence of left hydroureteronephrosis. Magnetic resonance imaging (MRI) and three-dimensional (3D) reconstruction revealed a 30 cm blind-ending ureter. Robotic-assisted excision (Da Vinci Xi) was performed safely, preserving adjacent structures. Histology confirmed a nonfunctional ureteral remnant. Conclusions: DBU is a rare duplication variant. Advanced imaging and robotic surgery are essential for accurate diagnosis and effective, minimally invasive treatment. More >

Jarrar Amjad¹, Muhammad Zaheer Sajid², Mudassir Khalil³, Ayman Youssef⁴, Muhammad Fareed Hamid⁵, Imran Qureshi^6,*, Haya Aldossary⁷, Qaisar Abbas⁶

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.2, 2026, DOI:10.32604/cmes.2026.073473 - 26 February 2026

Abstract Generative Adversarial Networks (GANs) have become valuable tools in medical imaging, enabling realistic image synthesis for enhancement, augmentation, and restoration. However, their integration into clinical workflows raises concerns, particularly the risk of subtle distortions or hallucinations that may undermine diagnostic accuracy and weaken trust in AI-assisted decision-making. To address this challenge, we propose a hybrid deep learning framework designed to detect GAN-induced artifacts in medical images, thereby reinforcing the reliability of AI-driven diagnostics. The framework integrates low-level statistical descriptors, including high-frequency residuals and Gray-Level Co-occurrence Matrix (GLCM) texture features, with high-level semantic representations extracted from… More >

Abdu Salam¹, Fahd M. Aldosari², Donia Y. Badawood³, Farhan Amin^4,*, Isabel de la Torre^5,*, Gerardo Mendez Mezquita⁶, Henry Fabian Gongora⁶

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.073342 - 10 February 2026

Abstract Lung cancer remains a major global health challenge, with early diagnosis crucial for improved patient survival. Traditional diagnostic techniques, including manual histopathology and radiological assessments, are prone to errors and variability. Deep learning methods, particularly Vision Transformers (ViT), have shown promise for improving diagnostic accuracy by effectively extracting global features. However, ViT-based approaches face challenges related to computational complexity and limited generalizability. This research proposes the DualSet ViT-PSO-SVM framework, integrating a ViT with dual attention mechanisms, Particle Swarm Optimization (PSO), and Support Vector Machines (SVM), aiming for efficient and robust lung cancer classification across multiple… More >

Nur Afikah Juhari, Khairul Nizam Tahar^*

Revue Internationale de Géomatique, Vol.35, pp. 1-11, 2026, DOI:10.32604/rig.2026.070991 - 19 January 2026

Abstract The use of Unmanned Aerial Vehicles (UAVs) in photogrammetry has grown rapidly due to enhanced flight stability, high-resolution imaging, and advanced Structure from Motion (SfM) algorithms. This study investigates the potential of UAVs as a cost-effective alternative to Terrestrial Laser Scanners (TLS) for 3D building reconstruction. A 3D model of Bangunan Sarjana was generated in Agisoft Metashape Professional v.2.0.2 using 492 aerial images captured at flying altitudes of 40, 50, and 60 m. Ground control points were established using GNSS (RTK-VRS), and Total Station measurements were employed for accuracy validation. The results indicate that the 60 More >

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 >