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 >

Tianzhe Jiao, Yuming Chen, Xiaoyue Feng, Chaopeng Guo, Jie Song^*

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2681-2700, 2025, DOI:10.32604/cmc.2025.069235 - 23 September 2025

Abstract Visible-infrared object detection leverages the day-night stable object perception capability of infrared images to enhance detection robustness in low-light environments by fusing the complementary information of visible and infrared images. However, the inherent differences in the imaging mechanisms of visible and infrared modalities make effective cross-modal fusion challenging. Furthermore, constrained by the physical characteristics of sensors and thermal diffusion effects, infrared images generally suffer from blurred object contours and missing details, making it difficult to extract object features effectively. To address these issues, we propose an infrared-visible image fusion network that realizes multimodal information fusion… More >

Jianjun Liu¹, Yang Li^2,*, Xiaoting Sun^3,*, Xiaohui Wang¹, Hanjiang Luo²

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3551-3568, 2025, DOI:10.32604/cmc.2025.066864 - 23 September 2025

Abstract Multimodal image fusion plays an important role in image analysis and applications. Multimodal medical image fusion helps to combine contrast features from two or more input imaging modalities to represent fused information in a single image. One of the critical clinical applications of medical image fusion is to fuse anatomical and functional modalities for rapid diagnosis of malignant tissues. This paper proposes a multimodal medical image fusion network (MMIF-Net) based on multiscale hybrid attention. The method first decomposes the original image to obtain the low-rank and significant parts. Then, to utilize the features at different More >

Muhammad Osama¹, Sarwar Shah Khan^2,*, Sajid Khan², Muzammil Khan³, Mian Muhammad Danyal⁴, Reshma Khan¹

Digital Engineering and Digital Twin, Vol.3, pp. 17-34, 2025, DOI:10.32604/dedt.2025.066344 - 19 August 2025

Abstract Image fusion is a technique used to combine essential information from two or more source images into a single, more informative output image. The resulting fused image contains more meaningful details than any individual source image. This study focuses on multi-focus image fusion, a crucial area in image processing. Due to the limited depth of field of optical lenses, it is often challenging to capture an image where all areas are in focus simultaneously. As a result, multi-focus image fusion plays a key role in integrating and extracting the necessary details from different focal regions.… More >

Kezhen Xie^1,2, Syed Mohd Zahid Syed Zainal Ariffin^1,*, Muhammad Izzad Ramli¹

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 997-1011, 2025, DOI:10.32604/cmc.2025.063469 - 09 June 2025

Abstract Infrared and visible image fusion technology integrates the thermal radiation information of infrared images with the texture details of visible images to generate more informative fused images. However, existing methods often fail to distinguish salient objects from background regions, leading to detail suppression in salient regions due to global fusion strategies. This study presents a mask-guided latent low-rank representation fusion method to address this issue. First, the GrabCut algorithm is employed to extract a saliency mask, distinguishing salient regions from background regions. Then, latent low-rank representation (LatLRR) is applied to extract deep image features, enhancing More >

Song Qian, Guzailinuer Yiming, Ping Li, Junfei Yang, Yan Xue, Shuping Zhang^*

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4069-4091, 2025, DOI:10.32604/cmc.2025.059931 - 06 March 2025

Abstract Infrared and visible light image fusion technology integrates feature information from two different modalities into a fused image to obtain more comprehensive information. However, in low-light scenarios, the illumination degradation of visible light images makes it difficult for existing fusion methods to extract texture detail information from the scene. At this time, relying solely on the target saliency information provided by infrared images is far from sufficient. To address this challenge, this paper proposes a lightweight infrared and visible light image fusion method based on low-light enhancement, named LLE-Fuse. The method is based on the… More >

Xuemei Yang¹, Yuting Zhou², Shiqi Liu¹, Junping Yin^2,3,*

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 3281-3307, 2025, DOI:10.32604/cmc.2024.057991 - 17 February 2025

Abstract Aiming at the problems of information loss and the relationship between features and target tasks in multimodal medical image segmentation, a multimodal medical image segmentation algorithm based on feature decoupling and information bottleneck theory is proposed in this paper. Based on the reversible network, the bottom-up learning method for different modal information is constructed, which enhances the features’ expression ability and the network’s learning ability. The feature fusion module is designed to balance multi-directional information flow. To retain the information relevant to the target task to the maximum extent and suppress the information irrelevant to… More >

Jeng-Shyang Pan^1,2, Wenda Li¹, Shu-Chuan Chu^1,*, Xiao Sui¹, Junzo Watada³

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 3033-3062, 2024, DOI:10.32604/cmc.2024.056310 - 18 November 2024

Abstract The growth optimizer (GO) is an innovative and robust metaheuristic optimization algorithm designed to simulate the learning and reflective processes experienced by individuals as they mature within the social environment. However, the original GO algorithm is constrained by two significant limitations: slow convergence and high memory requirements. This restricts its application to large-scale and complex problems. To address these problems, this paper proposes an innovative enhanced growth optimizer (eGO). In contrast to conventional population-based optimization algorithms, the eGO algorithm utilizes a probabilistic model, designated as the virtual population, which is capable of accurately replicating the… More >

Chun-Ming Wu¹, Mei-Ling Ren^2,*, Jin Lei², Zi-Mu Jiang³

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2857-2872, 2024, DOI:10.32604/cmc.2024.053708 - 15 August 2024

Abstract To address the issues of incomplete information, blurred details, loss of details, and insufficient contrast in infrared and visible image fusion, an image fusion algorithm based on a convolutional autoencoder is proposed. The region attention module is meant to extract the background feature map based on the distinct properties of the background feature map and the detail feature map. A multi-scale convolution attention module is suggested to enhance the communication of feature information. At the same time, the feature transformation module is introduced to learn more robust feature representations, aiming to preserve the integrity of… More >

Mian Muhammad Danyal^1,2, Sarwar Shah Khan^3,4,*, Rahim Shah Khan⁵, Saifullah Jan², Naeem ur Rahman⁶

Journal of Intelligent Medicine and Healthcare, Vol.2, pp. 35-53, 2024, DOI:10.32604/jimh.2024.051340 - 08 July 2024

Abstract Multi-modality medical images are essential in healthcare as they provide valuable insights for disease diagnosis and treatment. To harness the complementary data provided by various modalities, these images are amalgamated to create a single, more informative image. This fusion process enhances the overall quality and comprehensiveness of the medical imagery, aiding healthcare professionals in making accurate diagnoses and informed treatment decisions. In this study, we propose a new hybrid pre-processing approach, Laplacian Filter + Discrete Fourier Transform (LF+DFT), to enhance medical images before fusion. The LF+DFT approach highlights key details, captures small information, and sharpens… More >