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 >

Byeongjun Kim^1,#, Seung-Hun Lee^2,#, Won-Du Chang^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 1237-1252, 2025, DOI:10.32604/cmes.2025.073530 - 26 November 2025

Abstract Side-scan sonar (SSS) is essential for acquiring high-resolution seafloor images over large areas, facilitating the identification of subsea objects. However, military security restrictions and the scarcity of subsea targets limit the availability of SSS data, posing challenges for Automatic Target Recognition (ATR) research. This paper presents an approach that uses Cycle-Consistent Generative Adversarial Networks (CycleGAN) to augment SSS images of key subsea objects, such as shipwrecks, aircraft, and drowning victims. The process begins by constructing 3D models to generate rendered images with realistic shadows from multiple angles. To enhance image quality, a shadow extractor and More >

Huihui Yang¹, Saif Ur Rehman Khan^2,*, Omair Bilal², Chao Chen^1,*, Ming Zhao²

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2401-2434, 2025, DOI:10.32604/cmes.2025.072148 - 26 November 2025

Abstract Detecting Alzheimer’s disease is essential for patient care, as an accurate diagnosis influences treatment options. Classifying dementia from non-dementia in brain MRIs is challenging due to features such as hippocampal atrophy, while manual diagnosis is susceptible to error. Optimal computer-aided diagnosis (CAD) systems are essential for improving accuracy and reducing misclassification risks. This study proposes an optimized ensemble method (CEOE-Net) that initiates with the selection of pre-trained models, including DenseNet121, ResNet50V2, and ResNet152V2 for unique feature extraction. Each selected model is enhanced with the inclusion of a channel attention (CA) block to improve the feature… More >

Jingcheng Lyu^1,2,#, Ruiyu Yue^1,2,#, Boyu Yang^1,2, Xuanhao Li^1,2, Jian Song^1,2,*

Canadian Journal of Urology, Vol.32, No.5, pp. 445-456, 2025, DOI:10.32604/cju.2025.068853 - 30 October 2025

Abstract Objectives: This study aimed to develop an artificial intelligence (AI)-based image recognition system using the nnU-Net adaptive neural network to assist clinicians in detecting magnetic resonance imaging (MRI)-invisible prostate cancer. The motivation stems from the diagnostic challenges, especially when MRI findings are inconclusive (Prostate Imaging Reporting and Data System [PI-RADS] score ≤ 3). Methods: We retrospectively included 150 patients who underwent systematic prostate biopsy at Beijing Friendship Hospital between January 2013 and January 2023. All were pathologically confirmed to have clinically significant prostate cancer, despite negative findings on preoperative MRI. A total of 1475 MRI… More >

Federico Greco^1,2,*, Marco Cataldo³, Valerio D’Andrea^2,4, Luca Pugliese⁵, Andrea Panunzio⁶, Alessandro Tafuri⁶, Bruno Beomonte Zobel^2,4, Carlo Augusto Mallio^2,4

Canadian Journal of Urology, Vol.32, No.5, pp. 433-443, 2025, DOI:10.32604/cju.2025.068390 - 30 October 2025

Abstract Background: Radiogenomics offers a non-invasive approach to correlate imaging features with tumor molecular profiles. This study aims to identify computed tomography (CT) imaging characteristics associated with positive NIPA-like domain containing 4 (NIPAL4) expression in clear cell renal cell carcinoma (ccRCC) and to develop a radiogenomic predictive model to support personalized risk stratification. Methods: A retrospective analysis was conducted on 241 ccRCC patients from The Cancer Genome Atlas (TCGA) and The Cancer Imaging Archive (TCIA) databases. Clinical, pathological, and CT features were compared between NIPAL4-positive and NIPAL4-negative groups. A penalized logistic regression model was built to… 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 >

Carlos Quiterio Gómez Muñoz¹, Fausto Pedro García Márquez^2,*, Jorge Bernabé Sanjuán³

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3369-3386, 2025, DOI:10.32604/cmes.2025.069225 - 30 September 2025

Abstract Due to the continuous increase in global energy demand, photovoltaic solar energy generation and associated maintenance requirements have significantly expanded. One critical maintenance challenge in photovoltaic installations is detecting hot spots, localized overheating defects in solar cells that drastically reduce efficiency and can lead to permanent damage. Traditional methods for detecting these defects rely on manual inspections using thermal imaging, which are costly, labor-intensive, and impractical for large-scale installations. This research introduces an automated hybrid system based on two specialized convolutional neural networks deployed in a cascaded architecture. The first convolutional neural network efficiently detects More >

Sadia Alam Shammi^1,2, Yanbo Huang^1,*, Weiwei Xie^1,2, Gary Feng¹, Haile Tewolde¹, Xin Zhang³, Johnie Jenkins¹, Mark Shankle⁴

Phyton-International Journal of Experimental Botany, Vol.94, No.9, pp. 2745-2766, 2025, DOI:10.32604/phyton.2025.068955 - 30 September 2025

Abstract Crop leaf area index (LAI) and biomass are two major biophysical parameters to measure crop growth and health condition. Measuring LAI and biomass in field experiments is a destructive method. Therefore, we focused on the application of unmanned aerial vehicles (UAVs) in agriculture, which is a cost and labor-efficient method. Hence, UAV-captured multispectral images were applied to monitor crop growth, identify plant bio-physical conditions, and so on. In this study, we monitored soybean crops using UAV and field experiments. This experiment was conducted at the MAFES (Mississippi Agricultural and Forestry Experiment Station) Pontotoc Ridge-Flatwoods Branch… More >