@Article{cmc.2025.073342,
AUTHOR = {Abdu Salam, Fahd M. Aldosari, Donia Y. Badawood, Farhan Amin, Isabel de la Torre, Gerardo Mendez Mezquita, Henry Fabian Gongora},
TITLE = {A Hybrid Vision Transformer with Attention Architecture for Efficient Lung Cancer Diagnosis},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {87},
YEAR = {2026},
NUMBER = {1},
PAGES = {--},
URL = {http://www.techscience.com/cmc/v87n1/66061},
ISSN = {1546-2226},
ABSTRACT = {Lung cancer remains a major global health challenge, with early diagnosis crucial for improved patient survival. Traditional diagnostic techniques, including manual histopathology and radiological assessments, are prone to errors and variability. Deep learning methods, particularly Vision Transformers (ViT), have shown promise for improving diagnostic accuracy by effectively extracting global features. However, ViT-based approaches face challenges related to computational complexity and limited generalizability. This research proposes the DualSet ViT-PSO-SVM framework, integrating a ViT with dual attention mechanisms, Particle Swarm Optimization (PSO), and Support Vector Machines (SVM), aiming for efficient and robust lung cancer classification across multiple medical image datasets. The study utilized three publicly available datasets: LIDC-IDRI, LUNA16, and TCIA, encompassing computed tomography (CT) scans and histopathological images. Data preprocessing included normalization, augmentation, and segmentation. Dual attention mechanisms enhanced ViT’s feature extraction capabilities. PSO optimized feature selection, and SVM performed classification. Model performance was evaluated on individual and combined datasets, benchmarked against CNN-based and standard ViT approaches. The DualSet ViT-PSO-SVM significantly outperformed existing methods, achieving superior accuracy rates of 97.85% (LIDC-IDRI), 98.32% (LUNA16), and 96.75% (TCIA). Cross-dataset evaluations demonstrated strong generalization capabilities and stability across similar imaging modalities. The proposed framework effectively bridges advanced deep learning techniques with clinical applicability, offering a robust diagnostic tool for lung cancer detection, reducing complexity, and improving diagnostic reliability and interpretability.},
DOI = {10.32604/cmc.2025.073342}
}