Figures Abstract Lung diseases, particularly lung cancer, remain a leading cause of mortality worldwide, accounting for approximately 1.8 million deaths annually. Early and accurate diagnosis is critical for improving patient outcomes. This study also introduces a unified platform for evaluating multiple convolutional neural network architectures and comparing them to a Vision Transformer model while utilizing a common tensor-based preprocessing pipeline for classifying lung cancer with CT/PET-CT imaging. To enhance model adaptability, all input images were initially converted into tensors prior to training, enabling implicit fine-tuning without altering the original architecture. The YOLOTransfer dataset, comprising diverse and annotated medical images, was used to benchmark model performance. Classical CNN models such as AlexNet, VGG-16, ResNet-50, DenseNet, and EfficientNet were compared against ViT in terms of accuracy, sensitivity, specificity, F1-score, and AUC-ROC. Among all models, ResNet-50 and EfficientNet achieved the highest accuracy, while the Vision Transformer showed competitive results in capturing complex global patterns. The findings highlight the complementary strengths of convolutional and transformer-based architectures for medical image analysis and demonstrate the feasibility of deep learning approaches for lung cancer detection. Citation: Asim N, Sirshar M, Khan MZ, Ejaz S, Khalid S, Aljubayri I, et al. (2026) Tensor enhanced chest cancer classification via CNN and Vision Transformer models. PLoS One 21(6): e0348863. https://doi.org/10.1371/journal.pone.0348863 Editor: Muhammad Mateen, Soochow University, CHINA Received: September 3, 2025; Accepted: April 22, 2026; Published: June 2, 2026 Copyright: © 2026 Asim et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability: All image files are available from the database Roboflow, “YoloTransfer Dataset,” It is available at: https://universe.roboflow.com/mehmet-fatih-akca/yolotransfer, 2024. Funding: The author(s) received no specific funding for this work. Competing interests: The