Wafaa H. Alwan^1,*, Sabah M. Alturfi²

Computer Systems Science and Engineering, Vol.49, pp. 419-434, 2025, DOI:10.32604/csse.2025.064195 - 30 April 2025

Abstract Plant diseases pose a significant challenge to global agricultural productivity, necessitating efficient and precise diagnostic systems for early intervention and mitigation. In this study, we propose a novel hybrid framework that integrates EfficientNet-B8, Vision Transformer (ViT), and Knowledge Graph Fusion (KGF) to enhance plant disease classification across 38 distinct disease categories. The proposed framework leverages deep learning and semantic enrichment to improve classification accuracy and interpretability. EfficientNet-B8, a convolutional neural network (CNN) with optimized depth and width scaling, captures fine-grained spatial details in high-resolution plant images, aiding in the detection of subtle disease symptoms. In… More >

Qingpeng Wei^1,#, Wenai Song^1,#, Lizhen Fu¹, Yi Lei², Qing Wang^2,*

Journal on Artificial Intelligence, Vol.7, pp. 39-53, 2025, DOI:10.32604/jai.2025.063188 - 23 April 2025

Abstract Pneumoconiosis, a prevalent occupational lung disease characterized by fibrosis and impaired lung function, necessitates early and accurate diagnosis to prevent further progression and ensure timely clinical intervention. This study investigates the potential application of the Vision Transformer (ViT) deep learning model for automated pneumoconiosis classification using digital radiography (DR) images. We utilized digital X-ray images from 934 suspected pneumoconiosis patients. A U-Net model was applied for lung segmentation, followed by Canny edge detection to divide the lungs into six anatomical regions. The segmented images were augmented and used to train the ViT model. Model component… More >

Xinyi Wang^*, Wanru Song, Chuanyan Hao, Feng Liu

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 3351-3368, 2025, DOI:10.32604/cmc.2025.062922 - 16 April 2025

Abstract As Deepfake technology continues to evolve, the distinction between real and fake content becomes increasingly blurred. Most existing Deepfake video detection methods rely on single-frame facial image features, which limits their ability to capture temporal differences between frames. Current methods also exhibit limited generalization capabilities, struggling to detect content generated by unknown forgery algorithms. Moreover, the diversity and complexity of forgery techniques introduced by Artificial Intelligence Generated Content (AIGC) present significant challenges for traditional detection frameworks, which must balance high detection accuracy with robust performance. To address these challenges, we propose a novel Deepfake detection… More >

Poonam Sharma¹, Bhisham Sharma^2,*, Dhirendra Prasad Yadav³, Surbhi Bhatia Khan^4,5,6,*, Ahlam Almusharraf⁷

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 3227-3245, 2025, DOI:10.32604/cmc.2025.062376 - 16 April 2025

Abstract Rapid and precise diagnostic tools for Monkeypox (Mpox) lesions are crucial for effective treatment because their symptoms are similar to those of other pox-related illnesses, like smallpox and chickenpox. The morphological similarities between smallpox, chickenpox, and monkeypox, particularly in how they appear as rashes and skin lesions, which can sometimes make diagnosis challenging. Chickenpox lesions appear in many simultaneous phases and are more diffuse, often beginning on the trunk. In contrast, monkeypox lesions emerge progressively and are typically centralized on the face, palms, and soles. To provide accessible diagnostics, this study introduces a novel method… 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 >

Faten S. Alamri¹, Tariq Sadad^2,*, Ahmed S. Almasoud³, Raja Atif Aurangzeb⁴, Amjad Khan³

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 1023-1039, 2025, DOI:10.32604/cmc.2025.061890 - 26 March 2025

Abstract Mango farming significantly contributes to the economy, particularly in developing countries. However, mango trees are susceptible to various diseases caused by fungi, viruses, and bacteria, and diagnosing these diseases at an early stage is crucial to prevent their spread, which can lead to substantial losses. The development of deep learning models for detecting crop diseases is an active area of research in smart agriculture. This study focuses on mango plant diseases and employs the ConvNeXt and Vision Transformer (ViT) architectures. Two datasets were used. The first, MangoLeafBD, contains data for mango leaf diseases such as… More >

Xiaoyan Shao¹, Jiaqi Han^1,*, Lingling Li^1,*, Xuezhuan Zhao^1,2,3,4, Jingjing Yan¹

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 595-617, 2025, DOI:10.32604/cmc.2025.060295 - 26 March 2025

Abstract The primary challenge in weakly supervised semantic segmentation is effectively leveraging weak annotations while minimizing the performance gap compared to fully supervised methods. End-to-end model designs have gained significant attention for improving training efficiency. Most current algorithms rely on Convolutional Neural Networks (CNNs) for feature extraction. Although CNNs are proficient at capturing local features, they often struggle with global context, leading to incomplete and false Class Activation Mapping (CAM). To address these limitations, this work proposes a Contextual Prototype-Based End-to-End Weakly Supervised Semantic Segmentation (CPEWS) model, which improves feature extraction by utilizing the Vision Transformer… More >

Armughan Ali^1,2, Hooria Shahbaz², Robertas Damaševičius^3,*

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 1367-1398, 2025, DOI:10.32604/cmc.2025.059301 - 26 March 2025

Abstract Skin cancer is the most prevalent cancer globally, primarily due to extensive exposure to Ultraviolet (UV) radiation. Early identification of skin cancer enhances the likelihood of effective treatment, as delays may lead to severe tumor advancement. This study proposes a novel hybrid deep learning strategy to address the complex issue of skin cancer diagnosis, with an architecture that integrates a Vision Transformer, a bespoke convolutional neural network (CNN), and an Xception module. They were evaluated using two benchmark datasets, HAM10000 and Skin Cancer ISIC. On the HAM10000, the model achieves a precision of 95.46%, an… More >

Wei Liu^1,*, Ao Zhang

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 3045-3068, 2025, DOI:10.32604/cmc.2024.058640 - 17 February 2025

Abstract Plant diseases present a significant threat to global agricultural productivity, endangering both crop yields and quality. Traditional detection methods largely rely on manual inspection, a process that is not only labor-intensive and time-consuming but also subject to subjective biases and dependent on operators’ expertise. Recent advancements in Transformer-based architectures have shown substantial progress in image classification tasks, particularly excelling in global feature extraction. However, despite their strong performance, the high computational complexity and large parameter requirements of Transformer models limit their practical application in plant disease detection. To address these constraints, this study proposes an… More >

Muhammad Armghan Latif¹, Zohaib Mushtaq^2,*, Saifur Rahman³, Saad Arif⁴, Salim Nasar Faraj Mursal³, Muhammad Irfan³, Haris Aziz⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.2, pp. 1667-1695, 2025, DOI:10.32604/cmes.2024.056850 - 27 January 2025

Abstract Ransomware attacks pose a significant threat to critical infrastructures, demanding robust detection mechanisms. This study introduces a hybrid model that combines vision transformer (ViT) and one-dimensional convolutional neural network (1DCNN) architectures to enhance ransomware detection capabilities. Addressing common challenges in ransomware detection, particularly dataset class imbalance, the synthetic minority oversampling technique (SMOTE) is employed to generate synthetic samples for minority class, thereby improving detection accuracy. The integration of ViT and 1DCNN through feature fusion enables the model to capture both global contextual and local sequential features, resulting in comprehensive ransomware classification. Tested on the UNSW-NB15 More >