Lord Amoah^1,2, Jinwei Wang^1,2,3,*, Bernard-Marie Onzo^1,2

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 1635-1660, 2025, DOI:10.32604/cmes.2025.063992 - 30 May 2025

Abstract Medical image segmentation, i.e., labeling structures of interest in medical images, is crucial for disease diagnosis and treatment in radiology. In reversible data hiding in medical images (RDHMI), segmentation consists of only two regions: the focal and nonfocal regions. The focal region mainly contains information for diagnosis, while the nonfocal region serves as the monochrome background. The current traditional segmentation methods utilized in RDHMI are inaccurate for complex medical images, and manual segmentation is time-consuming, poorly reproducible, and operator-dependent. Implementing state-of-the-art deep learning (DL) models will facilitate key benefits, but the lack of domain-specific labels… More >

Jion Kim, Jayeon Kim, Byeong-Seok Shin^*

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5429-5443, 2025, DOI:10.32604/cmc.2025.063815 - 19 May 2025

Abstract Research has been conducted to reduce resource consumption in 3D medical image segmentation for diverse resource-constrained environments. However, decreasing the number of parameters to enhance computational efficiency can also lead to performance degradation. Moreover, these methods face challenges in balancing global and local features, increasing the risk of errors in multi-scale segmentation. This issue is particularly pronounced when segmenting small and complex structures within the human body. To address this problem, we propose a multi-stage hierarchical architecture composed of a detector and a segmentor. The detector extracts regions of interest (ROIs) in a 3D image, while More >

He Su, Jianwei Gao, Kang Kong^*

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4193-4213, 2025, DOI:10.32604/cmc.2025.063355 - 19 May 2025

Abstract Accurate and robust navigation in complex surgical environments is crucial for bronchoscopic surgeries. This study purposes a bronchoscopic lumen feature matching network (BLFM-Net) based on deep learning to address the challenges of image noise, anatomical complexity, and the stringent real-time requirements. The BLFM-Net enhances bronchoscopic image processing by integrating several functional modules. The FFA-Net preprocessing module mitigates image fogging and improves visual clarity for subsequent processing. The feature extraction module derives multi-dimensional features, such as centroids, area, and shape descriptors, from dehazed images. The Faster R-CNN Object detection module detects bronchial regions of interest and… More >

Honglin Wang¹, Yaohua Xu^2,*, Cheng Zhu³

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4999-5018, 2025, DOI:10.32604/cmc.2025.061848 - 19 May 2025

Abstract Medical image classification is crucial in disease diagnosis, treatment planning, and clinical decision-making. We introduced a novel medical image classification approach that integrates Bayesian Random Semantic Data Augmentation (BSDA) with a Vision Mamba-based model for medical image classification (MedMamba), enhanced by residual connection blocks, we named the model BSDA-Mamba. BSDA augments medical image data semantically, enhancing the model’s generalization ability and classification performance. MedMamba, a deep learning-based state space model, excels in capturing long-range dependencies in medical images. By incorporating residual connections, BSDA-Mamba further improves feature extraction capabilities. Through comprehensive experiments on eight medical image More >

Louyi Jiang^1,#, Sulei Wang^1,#, Jiang Xie¹, Haiya Wang², Wei Shao^3,*

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5445-5460, 2025, DOI:10.32604/cmc.2025.061759 - 19 May 2025

Abstract Carotid artery plaques represent a major contributor to the morbidity and mortality associated with cerebrovascular disease, and their clinical significance is largely determined by the risk linked to plaque vulnerability. Therefore, classifying plaque risk constitutes one of the most critical tasks in the clinical management of this condition. While classification models derived from individual medical centers have been extensively investigated, these single-center models often fail to generalize well to multi-center data due to variations in ultrasound images caused by differences in physician expertise and equipment. To address this limitation, a Dual-Classifier Label Correction Network model… More >

Jasem Almotiri^*

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2109-2142, 2025, DOI:10.32604/cmc.2025.062923 - 16 April 2025

Abstract The evolving field of Alzheimer’s disease (AD) diagnosis has greatly benefited from deep learning models for analyzing brain magnetic resonance (MR) images. This study introduces Dynamic GradNet, a novel deep learning model designed to increase diagnostic accuracy and interpretability for multiclass AD classification. Initially, four state-of-the-art convolutional neural network (CNN) architectures, the self-regulated network (RegNet), residual network (ResNet), densely connected convolutional network (DenseNet), and efficient network (EfficientNet), were comprehensively compared via a unified preprocessing pipeline to ensure a fair evaluation. Among these models, EfficientNet consistently demonstrated superior performance in terms of accuracy, precision, recall, and… More >

Mustafa Lateef Fadhil Jumaili^1,2, Emrullah Sonuç^1,*

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2947-2969, 2025, DOI:10.32604/cmc.2025.062719 - 16 April 2025

Abstract Alzheimer’s disease (AD) is a significant challenge in modern healthcare, with early detection and accurate staging remaining critical priorities for effective intervention. While Deep Learning (DL) approaches have shown promise in AD diagnosis, existing methods often struggle with the issues of precision, interpretability, and class imbalance. This study presents a novel framework that integrates DL with several eXplainable Artificial Intelligence (XAI) techniques, in particular attention mechanisms, Gradient-Weighted Class Activation Mapping (Grad-CAM), and Local Interpretable Model-Agnostic Explanations (LIME), to improve both model interpretability and feature selection. The study evaluates four different DL architectures (ResMLP, VGG16, Xception, More >

Omar Alqahtani, Mohamed Ghouse^*, Asfia Sabahath, Omer Bin Hussain, Arshiya Begum

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2221-2244, 2025, DOI:10.32604/cmc.2025.061977 - 16 April 2025

Abstract This paper introduces a novel method for medical image retrieval and classification by integrating a multi-scale encoding mechanism with Vision Transformer (ViT) architectures and a dynamic multi-loss function. The multi-scale encoding significantly enhances the model’s ability to capture both fine-grained and global features, while the dynamic loss function adapts during training to optimize classification accuracy and retrieval performance. Our approach was evaluated on the ISIC-2018 and ChestX-ray14 datasets, yielding notable improvements. Specifically, on the ISIC-2018 dataset, our method achieves an F1-Score improvement of +4.84% compared to the standard ViT, with a precision increase of +5.46% More >

Zeric Tabekoueng Njitacke¹, Louai A. Maghrabi², Musheer Ahmad^3,*, Turki Althaqafi⁴

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 917-933, 2025, DOI:10.32604/cmc.2025.059640 - 26 March 2025

Abstract This paper presents a high-security medical image encryption method that leverages a novel and robust sine-cosine map. The map demonstrates remarkable chaotic dynamics over a wide range of parameters. We employ nonlinear analytical tools to thoroughly investigate the dynamics of the chaotic map, which allows us to select optimal parameter configurations for the encryption process. Our findings indicate that the proposed sine-cosine map is capable of generating a rich variety of chaotic attractors, an essential characteristic for effective encryption. The encryption technique is based on bit-plane decomposition, wherein a plain image is divided into distinct… More >

Jianuo Huang^1,2, Bohan Lai², Weiye Qiu³, Caixu Xu⁴, Jie He^1,5,*

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4841-4862, 2025, DOI:10.32604/cmc.2025.059733 - 06 March 2025

Abstract The self-attention mechanism of Transformers, which captures long-range contextual information, has demonstrated significant potential in image segmentation. However, their ability to learn local, contextual relationships between pixels requires further improvement. Previous methods face challenges in efficiently managing multi-scale features of different granularities from the encoder backbone, leaving room for improvement in their global representation and feature extraction capabilities. To address these challenges, we propose a novel Decoder with Multi-Head Feature Receptors (DMHFR), which receives multi-scale features from the encoder backbone and organizes them into three feature groups with different granularities: coarse, fine-grained, and full set.… More >