Nizar Zaghden¹, Emad Ibrahim², Mukaram Safaldin^2,*, Mahmoud Mejdoub³

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 1117-1147, 2025, DOI:10.32604/cmc.2025.061948 - 26 March 2025

Abstract The increasing elderly population has heightened the need for accurate and reliable fall detection systems, as falls can lead to severe health complications. Existing systems often suffer from high false positive and false negative rates due to insufficient training data and suboptimal detection techniques. This study introduces an advanced fall detection model integrating YOLOv8, Faster R-CNN, and Generative Adversarial Networks (GANs) to enhance accuracy and robustness. A modified YOLOv8 architecture serves as the core, utilizing spatial attention mechanisms to improve critical image regions’ detection. Faster R-CNN is employed for fine-grained human posture analysis, while GANs… More >

Tariq Mahmood^1,2, Tanzila Saba¹, Faten S. Alamri^3,*, Alishba Tahir⁴, Noor Ayesha⁵

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 1173-1193, 2025, DOI:10.32604/cmc.2025.061150 - 26 March 2025

Abstract Innovation in learning algorithms has made retinal vessel segmentation and automatic grading techniques crucial for clinical diagnosis and prevention of diabetic retinopathy. The traditional methods struggle with accuracy and reliability due to multi-scale variations in retinal blood vessels and the complex pathological relationship in fundus images associated with diabetic retinopathy. While the single-modal diabetic retinopathy grading network addresses class imbalance challenges and lesion representation in fundus image data, dual-modal diabetic retinopathy grading methods offer superior performance. However, the scarcity of dual-modal data and the lack of effective feature fusion methods limit their potential due to… More >

Jia Liu¹, Hang Gu¹, Fangmei Liu¹, Hao Chen¹, Zuhe Li¹, Gang Xu², Qidong Liu², Wei Wang^2,*

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 803-822, 2025, DOI:10.32604/cmc.2025.060966 - 26 March 2025

Abstract In recent years, convolutional neural networks (CNN) and Transformer architectures have made significant progress in the field of remote sensing (RS) change detection (CD). Most of the existing methods directly stack multiple layers of Transformer blocks, which achieves considerable improvement in capturing variations, but at a rather high computational cost. We propose a channel-Efficient Change Detection Network (CE-CDNet) to address the problems of high computational cost and imbalanced detection accuracy in remote sensing building change detection. The adaptive multi-scale feature fusion module (CAMSF) and lightweight Transformer decoder (LTD) are introduced to improve the change detection More >

Zhiyong Li, Xinlian Zhou^*

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 739-760, 2025, DOI:10.32604/cmc.2025.059807 - 26 March 2025

Abstract Brain tumor classification is crucial for personalized treatment planning. Although deep learning-based Artificial Intelligence (AI) models can automatically analyze tumor images, fine details of small tumor regions may be overlooked during global feature extraction. Therefore, we propose a brain tumor Magnetic Resonance Imaging (MRI) classification model based on a global-local parallel dual-branch structure. The global branch employs ResNet50 with a Multi-Head Self-Attention (MHSA) to capture global contextual information from whole brain images, while the local branch utilizes VGG16 to extract fine-grained features from segmented brain tumor regions. The features from both branches are processed through More >

Zhenyang He, Mengjun Tong^*

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4301-4317, 2025, DOI:10.32604/cmc.2025.060550 - 06 March 2025

Abstract Tomato plant diseases often first manifest on the leaves, making the detection of tomato leaf diseases particularly crucial for the tomato cultivation industry. However, conventional deep learning models face challenges such as large model sizes and slow detection speeds when deployed on resource-constrained platforms and agricultural machinery. This paper proposes a lightweight model for detecting tomato leaf diseases, named LT-YOLO, based on the YOLOv8n architecture. First, we enhance the C2f module into a RepViT Block (RVB) with decoupled token and channel mixers to reduce the cost of feature extraction. Next, we incorporate a novel Efficient… More >

Woongkyu Park¹, Yeongyu Choi², Mahammad Shareef Mekala³, Gyu Sang Choi¹, Kook-Yeol Yoo¹, Ho-youl Jung^1,*

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 3965-3981, 2025, DOI:10.32604/cmc.2025.059966 - 06 March 2025

Abstract Designing fast and accurate neural networks is becoming essential in various vision tasks. Recently, the use of attention mechanisms has increased, aimed at enhancing the vision task performance by selectively focusing on relevant parts of the input. In this paper, we concentrate on squeeze-and-excitation (SE)-based channel attention, considering the trade-off between latency and accuracy. We propose a variation of the SE module, called squeeze-and-excitation with layer normalization (SELN), in which layer normalization (LN) replaces the sigmoid activation function. This approach reduces the vanishing gradient problem while enhancing feature diversity and discriminability of channel attention. In… More >

Naikang Zhong¹, Xiao Lin^1,2,3,4,*, Wen Du⁵, Jin Shi⁶

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 5285-5306, 2025, DOI:10.32604/cmc.2025.059102 - 06 March 2025

Abstract Multi-label image classification is a challenging task due to the diverse sizes and complex backgrounds of objects in images. Obtaining class-specific precise representations at different scales is a key aspect of feature representation. However, existing methods often rely on the single-scale deep feature, neglecting shallow and deeper layer features, which poses challenges when predicting objects of varying scales within the same image. Although some studies have explored multi-scale features, they rarely address the flow of information between scales or efficiently obtain class-specific precise representations for features at different scales. To address these issues, we propose… More >

Chengzhang Zhu^1,2, Ahmed Alasri¹, Tao Xu³, Yalong Xiao^1,2,*, Abdulrahman Noman¹, Raeed Alsabri¹, Xuanchu Duan⁴, Monir Abdullah⁵

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 5153-5167, 2025, DOI:10.32604/cmc.2024.058647 - 06 March 2025

Abstract Globally, diabetic retinopathy (DR) is the primary cause of blindness, affecting millions of people worldwide. This widespread impact underscores the critical need for reliable and precise diagnostic techniques to ensure prompt diagnosis and effective treatment. Deep learning-based automated diagnosis for diabetic retinopathy can facilitate early detection and treatment. However, traditional deep learning models that focus on local views often learn feature representations that are less discriminative at the semantic level. On the other hand, models that focus on global semantic-level information might overlook critical, subtle local pathological features. To address this issue, we propose an… More >

Xin Liu^1,*, Meng Sun¹, Bo Lin², Shibo Gu¹

Energy Engineering, Vol.122, No.3, pp. 1053-1072, 2025, DOI:10.32604/ee.2025.060489 - 07 March 2025

Abstract Long-term petroleum production forecasting is essential for the effective development and management of oilfields. Due to its ability to extract complex patterns, deep learning has gained popularity for production forecasting. However, existing deep learning models frequently overlook the selective utilization of information from other production wells, resulting in suboptimal performance in long-term production forecasting across multiple wells. To achieve accurate long-term petroleum production forecast, we propose a spatial-geological perception graph convolutional neural network (SGP-GCN) that accounts for the temporal, spatial, and geological dependencies inherent in petroleum production. Utilizing the attention mechanism, the SGP-GCN effectively captures… More >

Yue Chen¹, Xiaoman Liang^1,2,*, Mugang Lin^1,2, Fachao Zhang¹, Huihuang Zhao^1,2

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2753-2768, 2025, DOI:10.32604/cmc.2024.059530 - 17 February 2025

Abstract The objective of image-based virtual try-on is to seamlessly integrate clothing onto a target image, generating a realistic representation of the character in the specified attire. However, existing virtual try-on methods frequently encounter challenges, including misalignment between the body and clothing, noticeable artifacts, and the loss of intricate garment details. To overcome these challenges, we introduce a two-stage high-resolution virtual try-on framework that integrates an attention mechanism, comprising a garment warping stage and an image generation stage. During the garment warping stage, we incorporate a channel attention mechanism to effectively retain the critical features of… More >