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 >

Thien-Tan Cao, Huu-Thanh Duong, Viet-Tuan Le, Hau Nguyen Trung, Vinh Truong Hoang, Kiet Tran-Trung^*

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4373-4391, 2025, DOI:10.32604/cmc.2025.061583 - 19 May 2025

Abstract Kinship verification is a key biometric recognition task that determines biological relationships based on physical features. Traditional methods predominantly use facial recognition, leveraging established techniques and extensive datasets. However, recent research has highlighted ear recognition as a promising alternative, offering advantages in robustness against variations in facial expressions, aging, and occlusions. Despite its potential, a significant challenge in ear-based kinship verification is the lack of large-scale datasets necessary for training deep learning models effectively. To address this challenge, we introduce the EarKinshipVN dataset, a novel and extensive collection of ear images designed specifically for kinship… More >

Daoming Wei¹, Fangyan Ge², Bopeng Zhang¹, Zhiqiang Zhao³, Dequan Li^3,*, Lizong Xi⁴, Jinrong Hu^5,*, Xin Wang⁶

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5729-5746, 2025, DOI:10.32604/cmc.2025.061402 - 19 May 2025

Abstract Accurate cloud classification plays a crucial role in aviation safety, climate monitoring, and localized weather forecasting. Current research has been focusing on machine learning techniques, particularly deep learning based model, for the types identification. However, traditional approaches such as convolutional neural networks (CNNs) encounter difficulties in capturing global contextual information. In addition, they are computationally expensive, which restricts their usability in resource-limited environments. To tackle these issues, we present the Cloud Vision Transformer (CloudViT), a lightweight model that integrates CNNs with Transformers. The integration enables an effective balance between local and global feature extraction. To… More >

Suhaila Abuowaida^1,*, Hamza Abu Owida², Deema Mohammed Alsekait^3,*, Nawaf Alshdaifat⁴, Diaa Salama AbdElminaam^5,6, Mohammad Alshinwan⁴

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 3303-3333, 2025, DOI:10.32604/cmc.2025.063470 - 16 April 2025

Abstract Segmenting a breast ultrasound image is still challenging due to the presence of speckle noise, dependency on the operator, and the variation of image quality. This paper presents the UltraSegNet architecture that addresses these challenges through three key technical innovations: This work adds three things: (1) a changed ResNet-50 backbone with sequential 3 convolutions to keep fine anatomical details that are needed for finding lesion boundaries; (2) a computationally efficient regional attention mechanism that works on high-resolution features without using a transformer’s extra memory; and (3) an adaptive feature fusion strategy that changes local and… More >

Jagadesh Balasubramani¹, Surendran Rajendran^1,*, Mohammad Zakariah², Abeer Alnuaim²

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2793-2807, 2025, DOI:10.32604/cmc.2025.062266 - 16 April 2025

Abstract Autism spectrum disorder (ASD) is a multifaceted neurological developmental condition that manifests in several ways. Nearly all autistic children remain undiagnosed before the age of three. Developmental problems affecting face features are often associated with fundamental brain disorders. The facial evolution of newborns with ASD is quite different from that of typically developing children. Early recognition is very significant to aid families and parents in superstition and denial. Distinguishing facial features from typically developing children is an evident manner to detect children analyzed with ASD. Presently, artificial intelligence (AI) significantly contributes to the emerging computer-aided… More >

Panru Liang¹, Guojiang Xin^1,*, Xiaolei Yi², Hao Liang³, Changsong Ding¹

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2481-2504, 2025, DOI:10.32604/cmc.2025.060961 - 16 April 2025

Abstract Automatic pancreas segmentation plays a pivotal role in assisting physicians with diagnosing pancreatic diseases, facilitating treatment evaluations, and designing surgical plans. Due to the pancreas’s tiny size, significant variability in shape and location, and low contrast with surrounding tissues, achieving high segmentation accuracy remains challenging. To improve segmentation precision, we propose a novel network utilizing EfficientNetV2 and multi-branch structures for automatically segmenting the pancreas from CT images. Firstly, an EfficientNetV2 encoder is employed to extract complex and multi-level features, enhancing the model’s ability to capture the pancreas’s intricate morphology. Then, a residual multi-branch dilated attention… More >

Min Yao^1,*, Guangjie Hu¹, Yaozu Zhang²

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2751-2771, 2025, DOI:10.32604/cmc.2025.060860 - 16 April 2025

Abstract Semantic segmentation of remote sensing images is a critical research area in the field of remote sensing. Despite the success of Convolutional Neural Networks (CNNs), they often fail to capture inter-layer feature relationships and fully leverage contextual information, leading to the loss of important details. Additionally, due to significant intra-class variation and small inter-class differences in remote sensing images, CNNs may experience class confusion. To address these issues, we propose a novel Category-Guided Feature Collaborative Learning Network (CG-FCLNet), which enables fine-grained feature extraction and adaptive fusion. Specifically, we design a Feature Collaborative Learning Module (FCLM)… More >

Pratik Jadhav¹, Vuppala Adithya Sairam¹, Niranjan Bhojane¹, Abhyuday Singh¹, Shilpa Gite^1,2, Biswajeet Pradhan^3,*, Mrinal Bachute¹, Abdullah Alamri⁴

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 3493-3517, 2025, DOI:10.32604/cmc.2025.060764 - 16 April 2025

Abstract Electronic nose and thermal images are effective ways to diagnose the presence of gases in real-time real-time. Multimodal fusion of these modalities can result in the development of highly accurate diagnostic systems. The low-cost thermal imaging software produces low-resolution thermal images in grayscale format, hence necessitating methods for improving the resolution and colorizing the images. The objective of this paper is to develop and train a super-resolution generative adversarial network for improving the resolution of the thermal images, followed by a sparse autoencoder for colorization of thermal images and a multimodal convolutional neural network for… More >

Chunlai Du^1,#, Xin Gu^1,#, Yanhui Guo^2,*, Siqi Guo³, Ziwei Pang³, Yi Du³, Guoqing Du^3,*

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 3435-3450, 2025, DOI:10.32604/cmc.2025.060212 - 16 April 2025

Abstract Coronary artery disease is a highly lethal cardiovascular condition, making early diagnosis crucial for patients. Echocardiograph is employed to identify coronary heart disease (CHD). However, due to issues such as fuzzy object boundaries, complex tissue structures, and motion artifacts in ultrasound images, it is challenging to detect CHD accurately. This paper proposes an improved Transformer model based on the Feedback Self-Attention Mechanism (FSAM) for classification of ultrasound images. The model enhances attention weights, making it easier to capture complex features. Experimental results show that the proposed method achieves high levels of accuracy, recall, precision, F1 More >

Jahanzaib Latif ¹, Ahsan Wajahat¹, Alishba Tahir², Anas Bilal^3,*, Mohammed Zakariah⁴, Abeer Alnuaim⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 539-567, 2025, DOI:10.32604/cmes.2025.062301 - 11 April 2025

Abstract Glaucoma, a leading cause of blindness, demands early detection for effective management. While AI-based diagnostic systems are gaining traction, their performance is often limited by challenges such as varying image backgrounds, pixel intensity inconsistencies, and object size variations. To address these limitations, we introduce an innovative, nature-inspired machine learning framework combining feature excitation-based dense segmentation networks (FEDS-Net) and an enhanced gray wolf optimization-supported support vector machine (IGWO-SVM). This dual-stage approach begins with FEDS-Net, which utilizes a fuzzy integral (FI) technique to accurately segment the optic cup (OC) and optic disk (OD) from retinal images, even More >