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The YOLOTransfer dataset benchmarks performance across AlexNet, VGG-16, ResNet-50, DenseNet, and EfficientNet, reporting accuracy, sensitivity, specificity, F1-score, and AUC-ROC.",{"@graph":14,"@context":72},[15,34,55],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & Report",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/tensor-enhanced-chest-cancer-classification-via-cnn-and-vision-transformer-models-read-online/345519/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/tensor-enhanced-chest-cancer-classification-via-cnn-and-vision-transformer-models-read-online/345519.png","ImageObject",300,407,{"name":42,"@type":43},"Levi","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-09-23","2026-09-22",true,{"@type":52,"interactionType":53,"userInteractionCount":26},"InteractionCounter",{"@type":54},"ViewAction",{"@type":56,"mainEntity":57},"FAQPage",[58,64,68],{"name":59,"@type":60,"acceptedAnswer":61},"What imaging data and preprocessing approach does the study use for classification?","Question",{"text":62,"@type":63},"The study classifies lung cancer using CT/PET-CT imaging and applies a unified tensor-based preprocessing pipeline, converting input images into tensors before training.","Answer",{"name":65,"@type":60,"acceptedAnswer":66},"Which models are compared in the experiments?",{"text":67,"@type":63},"Classical CNN models (AlexNet, VGG-16, ResNet-50, DenseNet, and EfficientNet) are compared against a Vision Transformer model for performance evaluation.",{"name":69,"@type":60,"acceptedAnswer":70},"Which metrics are used to assess model performance?",{"text":71,"@type":63},"Performance is measured using accuracy, sensitivity, specificity, F1-score, and AUC-ROC.","https://schema.org",{"og:url":32,"og:type":74,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":76,"canonical":32},"index,follow",{"doc_id":78,"site_id":7},345519,1790194547,{"code":4,"msg":81,"data":82},"success",[83,87,91,95,100,105,110,114,119,122,126],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":84,"show_sort_weight":85,"slug":86},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":88,"show_sort_weight":89,"slug":90},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":92,"show_sort_weight":93,"slug":94},"Exam",70,"exam",{"id":96,"doc_module":4,"doc_module_name":25,"category_name":97,"show_sort_weight":98,"slug":99},5,"Comic",60,"comic",{"id":101,"doc_module":4,"doc_module_name":25,"category_name":102,"show_sort_weight":103,"slug":104},6,"Technology",50,"technology",{"id":106,"doc_module":4,"doc_module_name":25,"category_name":107,"show_sort_weight":108,"slug":109},7,"Healthcare",40,"healthcare",{"id":111,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":112,"slug":113},8,30,"research-report",{"id":115,"doc_module":4,"doc_module_name":25,"category_name":116,"show_sort_weight":117,"slug":118},9,"Religion & Spirituality",20,"religion-spirituality",{"id":117,"doc_module":4,"doc_module_name":25,"category_name":120,"show_sort_weight":117,"slug":121},"World Cup","world-cup",{"id":123,"doc_module":4,"doc_module_name":25,"category_name":124,"show_sort_weight":123,"slug":125},10,"Lifestyle","lifestyle",{"id":127,"doc_module":4,"doc_module_name":25,"category_name":128,"show_sort_weight":96,"slug":129},19,"General","general",{"code":4,"msg":81,"data":131},{"doc_id":78,"user_id":132,"nickname":42,"user_avatar":133,"doc_module":4,"category_id":111,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":26,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":139,"language":140,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":12,"update_tm":144,"read_time":145},7971461740909,"https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d","OPEN ACCESS  \nCitation: 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/](https://doi.org/10.1371/)[ ](https://doi.org/10.1371/)[journal.pone.0348863](journal.pone.0348863)  \nEditor: Muhammad Mateen, Soochow University, CHINA  \nReceived: September 3, 2025  \nAccepted: April 22, 2026  \nPublished: June 2, 2026  \nCopyright: © 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.  \nData availability statement: All image files are available from the database Roboflow,“YoloTransfer Dataset,” It is available at: [https://](https://)[ ](https://)[universe.roboflow.com/mehmet-fatih-akca/](universe.roboflow.com/mehmet-fatih-akca/)[ ](universe.roboflow.com/mehmet-fatih-akca/)[yolotransfer](yolotransfer), 2024.  \nFunding: The author(s) received no specific funding for this work.  \nRESEARCH ARTICLE  \nTensor enhanced chest cancer classification via CNN and Vision Transformer models  \nNayab Asim1☯, Mehreen Sirshar1☯*, Mohammad Zubair Khan2*, Sidra Ejaz1, Sobia Khalid1, Ibrahim Aljubayri3, Abdulrahman Alahmadi4,5  \n1 Software Engineering, Fatima Jinnah Women University, Rawalpindi, Punjab, Pakistan, 2 Faculty of Computer and Information Systems, Islamic University of Madinah, Medina, Saudi Arabia, 3 Department of Computer Science and Information, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia, 4 Department of Computer Science and Information, Applied College, Taibah University, Madinah, Saudi Arabia, 5 Energy, Industry, and Advanced Technologies Research Center, Taibah University, Madinah, Saudi Arabia  \n☯ These authors contributed equally to this work.  \n* [mehreensirshar@fjwu.edu.pk](mehreensirshar@fjwu.edu.pk) (MS); [zubair.762001@gmail.com](zubair.762001@gmail.com) (MZK)  \nAbstract  \nLung 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  \ntensor-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  \ntransformer-based architectures for medical image analysis and demonstrate the feasibility of deep learning approaches for lung cancer detection.  \nIntroduction  \nThe lungs are vital organs responsible for gas exchange, delivering oxygen to tissues and removing carbon dioxide, yet this essential system is highly vulnerable to malignant disease. Lung cancer remains the leading cause of cancer death worldwide, accounting for an estimated 2.5 million new cases and 1.8 million deaths in 2022 [ 1] . Globally, lung cancer contributes nearly one in eight new cancer diagnoses and  \nPLOS One | [https://doi.org/10.1371/journal.pone.0348863](https://doi.org/10.1371/journal.pone.0348863) June 2, 2026 1 / ","cbCaik4lsE06raFa","https://ap.wps.com/l/cbCaik4lsE06raFa","pdf",1879700,17,"English","# Abstract\n# Introduction\n## Lung cancer burden and diagnosis challenges\n## Imaging and screening role","[{\"question\":\"What imaging data and preprocessing approach does the study use for classification?\",\"answer\":\"The study classifies lung cancer using CT/PET-CT imaging and applies a unified tensor-based preprocessing pipeline, converting input images into tensors before training.\"},{\"question\":\"Which models are compared in the experiments?\",\"answer\":\"Classical CNN models (AlexNet, VGG-16, ResNet-50, DenseNet, and EfficientNet) are compared against a Vision Transformer model for performance evaluation.\"},{\"question\":\"Which metrics are used to assess model performance?\",\"answer\":\"Performance is measured using accuracy, sensitivity, specificity, F1-score, and AUC-ROC.\"}]","Tensor enhanced chest cancer classification via CNN and Vision Transformer models - read online | PDF",1790057954,43]