Nahdi Saubari^1,2,*, Kunfeng Wang^1,*, Rachmat Muwardi^3,*, Andri Pranolo⁴

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077087 - 09 April 2026

Abstract This study proposes an Adaptive Pooling method based on an alpha (α) parameter to enhance the effectiveness and stability of convolutional neural networks (CNNs) in image classification tasks. Conventional pooling techniques, such as max pooling and average pooling, often exhibit limited adaptability when applied to datasets with heterogeneous distributions and varying levels of complexity. To address this limitation, the proposed approach introduces an α parameter ranging from 0 to 1 that continuously regulates the contribution of maximum-based and average-based pooling operations in a unified and flexible framework. The proposed method is evaluated using two benchmark… More >

Sultan Kahla¹, Zuping Zhang^1,*, Majed Alsafyani², Ahmed Emara^3,*, Mohammod Abdullah Bin Hossain⁴, Abdulwahab Osman Sheikhdon¹

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.076402 - 09 April 2026

Abstract The effective diagnosis and treatment planning require the correct classification of the cerebral neoplasia, such as glioma, meningioma, and pituitary tumors. The recent developments in the deep learning field have made a significant contribution to the field of image analysis in medicine; however, Vision Transformers (ViTs) have achieved good results but are computationally complex. This paper presents NeuroTriad-ViT, a proprietary large-scale Vision Transformer of 235 million parameters, which is represented as a high-performance teacher model to classify brain tumors. Knowledge distillation is applied in an attempt to transfer the representations that the teacher learned to… More >

Swetha K¹, Shiloah Elizabeth Darmanayagam^1,*, Sunil Retmin Raj Cyril²

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.077293 - 30 March 2026

Abstract Recognizing frontal faces from non-frontal or profile images is a major problem due to pose changes, self-occlusions, and the complete loss of important structural and textural components, depressing recognition accuracy and visual fidelity. This paper introduces a new deep generative framework, Modified Multi-Scale Fused CycleGAN (MMF-CycleGAN), for robust and photo-realistic profile-to-frontal face synthesis. The MMF-CycleGAN framework utilizes pre-processing and then the generator employs a Deep Dilated DenseNet encoder-based hierarchical feature extraction along with a transformer and decoder. The proposed Multi-Scale Fusion PatchGAN discriminator enforces consistency at multiple spatial resolutions, leading to sharper textures and improved More > Graphic Abstract

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

Huda F. Al-Shahad^1,2, Razali Yaakob^1,*, Nurfadhlina Mohd Sharef¹, Hazlina Hamdan¹, Hasyma Abu Hassan³, Xiaoyi Jiang⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2025.074109 - 30 March 2026

Abstract Currently, thyroid diseases are prevalent worldwide; therefore, it is necessary to develop techniques that help doctors improve their diagnostic skills for such diseases. In previous studies, 2-dimensional convolutional neural network (2D CNN) techniques were employed to classify thyroid nodules as benign and malignant without detecting the presence of thyroid nodules in the obtained ultrasound images. To address this issue, we propose a 3-dimensional convolutional neural network (3D CNN) for thyroid nodule detection. The proposed CNN exploits the 3D information and spatial features contained in ultrasound images and generates distinctive features during its training using multiple… More >

Atif Mahmood^1,*, Yasin Saleem¹, Usman Tariq², Yousef Ibrahim Daradkeh³, Adnan N. Qureshi⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2025.073501 - 30 March 2026

Abstract Federated learning is a decentralized model training paradigm with significant potential. However, the quality of Federated Network’s client updates can vary due to non-IID data distributions, leading to suboptimal global models. To address this issue, we propose a novel client selection strategy called FedPA (Performance-Based Federated Averaging). This proposed model selectively aggregates client updates based on a predefined performance threshold. Only clients whose local models achieve an F1 score of 70% or higher after training are included in the aggregation process. Clients below this threshold receive the updated global model but do not contribute their… More >

Garip Erayabakan^1,*, Burak Canpolat²

International Journal of Mental Health Promotion, Vol.28, No.3, 2026, DOI:10.32604/ijmhp.2026.076595 - 31 March 2026

Abstract Background: Social media plays an important role in shaping body image and self-perception, particularly among appearance-sensitive groups such as athletes. Although problematic social media use has been linked to body image outcomes through processes such as social comparison, self-presentation, and evaluation sensitivity, these mechanisms remain underexplored among athletes with physical disabilities. This study aimed to examine the associations between social media use, addictive use patterns, and body image perception in this population, with a focus on these underlying psychological mechanisms. Methods: A total of 165 athletes with physical disability participated in this quantitative cross-sectional study. Data… More >

Long Li, Hengyang Wu^*, Na Wang

Journal on Artificial Intelligence, Vol.8, pp. 169-182, 2026, DOI:10.32604/jai.2026.077462 - 23 March 2026

Abstract Zero-shot learning as an emerging approach in image captioning techniques, has garnered significant attention from researchers in recent years due to its ability to accomplish tasks without requiring specific category training data. Existing zero-shot image captioning schemes largely rely on traditional language models, which exhibit low efficiency and suboptimal generation quality. To address this issue, this study proposes Hamiltonian Monte Carlo for Image Captioning (HMCIC). This method first models the image captioning task as a probabilistic sampling problem in parameter space, integrating semantic matching and syntactic coherence into an energy function to guide the generation… More >

Wasfieh Nazzal¹, Ezequiel López-Rubio^1,2,3, Miguel A. Molina-Cabello^1,2,3, Karl Thurnhofer-Hemsi^1,2,3,*

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.077561 - 12 March 2026

Abstract Automatic and accurate medical image segmentation remains a fundamental task in computer-aided diagnosis and treatment planning. Recent advances in foundation models, such as the medical-focused Segment Anything Model (MedSAM), have demonstrated strong performance but face challenges in many medical applications due to anatomical complexity and a limited domain-specific prompt. This work introduces a methodology that enhances segmentation robustness and precision by automatically generating multiple informative point prompts, rather than relying on single inputs. The proposed approach randomly samples sets of spatially distributed point prompts based on image features, enabling MedSAM to better capture fine-grained anatomical… More >

Xi Cai, Xiaoqiang Wang, Huiying Zhao, Guang Han^*

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.075756 - 12 March 2026

Abstract Low-light image enhancement (LLIE) remains challenging due to underexposure, color distortion, and amplified noise introduced during illumination correction. Existing deep learning–based methods typically apply uniform enhancement across the entire image, which overlooks scene semantics and often leads to texture degradation or unnatural color reproduction. To overcome these limitations, we propose a Semantic-Guided Visual Mamba Network (SGVMNet) that unifies semantic reasoning, state-space modeling, and mixture-of-experts routing for adaptive illumination correction. SGVMNet comprises three key components: (1) a semantic modulation module (SMM) that extracts scene-aware semantic priors from pretrained multimodal models—Large Language and Vision Assistant (LLaVA) and… More >