Xiaofeng Liu^*, Yubin Bao, Fangling Leng

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3503-3517, 2025, DOI:10.32604/cmc.2025.064544 - 03 July 2025

Abstract The rapid development of evolutionary deep learning has led to the emergence of various Neural Architecture Search (NAS) algorithms designed to optimize neural network structures. However, these algorithms often face significant computational costs due to the time-consuming process of training neural networks and evaluating their performance. Traditional NAS approaches, which rely on exhaustive evaluations and large training datasets, are inefficient for solving complex image classification tasks within limited time frames. To address these challenges, this paper proposes a novel NAS algorithm that integrates a hierarchical evaluation strategy based on Surrogate models, specifically using supernet to… More >

Min Ji^*, Shucheng Yang

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3075-3103, 2025, DOI:10.32604/cmc.2025.064166 - 03 July 2025

Abstract In agricultural production, crop images are commonly used for the classification and identification of various crops. However, several challenges arise, including low image clarity, elevated noise levels, low accuracy, and poor robustness of existing classification models. To address these issues, this research proposes an innovative crop image classification model named Lap-FEHRNet, which integrates a Laplacian Pyramid Super Resolution Network (LapSRN) with a feature enhancement high-resolution network based on attention mechanisms (FEHRNet). To mitigate noise interference, this research incorporates the LapSRN network, which utilizes a Laplacian pyramid structure to extract multi-level feature details from low-resolution images… 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 >

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 >

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 >

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 >

Yi Zuo, Zhenping Chen^*, Jing Feng, Yunhao Fan

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4649-4667, 2025, DOI:10.32604/cmc.2025.059472 - 06 March 2025

Abstract Image classification is crucial for various applications, including digital construction, smart manufacturing, and medical imaging. Focusing on the inadequate model generalization and data privacy concerns in few-shot image classification, in this paper, we propose a federated learning approach that incorporates privacy-preserving techniques. First, we utilize contrastive learning to train on local few-shot image data and apply various data augmentation methods to expand the sample size, thereby enhancing the model’s generalization capabilities in few-shot contexts. Second, we introduce local differential privacy techniques and weight pruning methods to safeguard model parameters, perturbing the transmitted parameters to ensure 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 >

Kun Liu¹, Chen Bao^1,*, Sidong Liu²

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4451-4468, 2025, DOI:10.32604/cmc.2024.059053 - 06 March 2025

Abstract Large amounts of labeled data are usually needed for training deep neural networks in medical image studies, particularly in medical image classification. However, in the field of semi-supervised medical image analysis, labeled data is very scarce due to patient privacy concerns. For researchers, obtaining high-quality labeled images is exceedingly challenging because it involves manual annotation and clinical understanding. In addition, skin datasets are highly suitable for medical image classification studies due to the inter-class relationships and the inter-class similarities of skin lesions. In this paper, we propose a model called Coalition Sample Relation Consistency (CSRC),… More >