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A new Cancer Nexus Synergy (CanNS) framework integrates Swin-Transformer UNet (SwiNet) for segmentation, Xception-LSTM GAN (XLG) CancerNet for classification, and Devilish Levy Optimization (DevLO) for fine-tuning. Results show improved accuracy, sensitivity, and specificity versus earlier methods.",{"@graph":14,"@context":73},[15,34,56],{"@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/healthcare/","Healthcare",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/enhancing-lung-cancer-detection-through-integrated-deep-learning-and-transformer-models/368452/",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/enhancing-lung-cancer-detection-through-integrated-deep-learning-and-transformer-models/368452.png","ImageObject",300,407,{"name":42,"@type":43},"\tCallum ","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-11-04","2026-09-23",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",8,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"Why is early lung cancer detection important?","Question",{"text":63,"@type":64},"Early diagnosis improves life expectancy by enabling timely interventions and better treatment options. It also supports early differentiation between benign nodules and possible malignancies.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"What limitations in existing deep learning methods motivate CanNS?",{"text":68,"@type":64},"Prior work often relies on large annotated data, can overfit, may be computationally heavy, and suffers from weak interpretability. Model stability on real clinical datasets also remains an open challenge.",{"name":70,"@type":61,"acceptedAnswer":71},"How does the CanNS framework work?",{"text":72,"@type":64},"CanNS combines Swin-Transformer UNet (SwiNet) for segmentation, Xception-LSTM GAN (XLG) CancerNet for classification, and Devilish Levy Optimization (DevLO) to fine-tune system parameters.","https://schema.org",{"og:url":32,"og:type":75,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":77,"canonical":32},"index,follow",{"doc_id":79,"site_id":7},368452,1790664030,{"code":4,"msg":82,"data":83},"success",[84,88,92,96,101,106,110,114,119,122,126],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":85,"show_sort_weight":86,"slug":87},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":89,"show_sort_weight":90,"slug":91},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":93,"show_sort_weight":94,"slug":95},"Exam",70,"exam",{"id":97,"doc_module":4,"doc_module_name":25,"category_name":98,"show_sort_weight":99,"slug":100},5,"Comic",60,"comic",{"id":102,"doc_module":4,"doc_module_name":25,"category_name":103,"show_sort_weight":104,"slug":105},6,"Technology",50,"technology",{"id":107,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":108,"slug":109},7,40,"healthcare",{"id":55,"doc_module":4,"doc_module_name":25,"category_name":111,"show_sort_weight":112,"slug":113},"Research & Report",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":97,"slug":129},19,"General","general",{"code":4,"msg":82,"data":131},{"doc_id":79,"user_id":132,"nickname":42,"user_avatar":133,"doc_module":4,"category_id":107,"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":55,"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},137451211410,"https://ap-avatar.wpscdn.com/avatar/2000bb0a9246f588df?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786362646172706240","[www. nature.com/scientificreports](www. nature.com/scientificreports)  \nOPEN  \nEnhancing lung cancer detection through integrated deep learning and transformer models  \nRevathi Durgam1, Bharathi Panduri2, V. Balaji3, Adil O. Khadidos4, Alaa O. Khadidos5,9 & Shitharth Selvarajan6,7,8􀀍  \nLung cancer has been stated as one of the prevalent killers of cancer up to this present time and this clearly underlines the rationale for early diagnosis to enhance life expectancy of patients afflicted with the condition. The reasons behind the usage of the transformer and deep learning classifiers for the detection of lung cancer include accuracy, robustness along with the capability to handle and evaluate large data sets and much more. Such models can be more complex and can help to utilize multiple modalities of data to give extensive information that will be critical in ascertaining the right diagnosis at the right time. However, the existing works encounter several limitations including reliance on large annotated data, overfitting, high computation complexity, and interpretability. Third, the issue of the stability of these models’ performance when applied to actual clinical datasets is still an open question; this is an even bigger issue that will greatly reduce the actual utilization of these models in clinical practice. To tackle these, we develop a novel Cancer Nexus Synergy (CanNS), which applies of A. Swin-Transformer UNet (SwiNet) Model for segmentation, Xception-LSTM GAN (XLG) CancerNet for classification, and Devilish Levy Optimization (DevLO) for fine-tuning parameters. This paper breaks new ground in that the presented elements are incorporated in a manner that co-operatively elevates the diagnostic capabilities while at the same time being computationally light and resilient. These are SwiNet for segmented analysis, XLG CancerNet for precise classification of the cases, and DevLO that optimizes the parameters of the lung cancer detection system, making the system more sensible and efficient. The performance outcomes indicate that the CanNS framework enhances the detection’s accuracy, sensitivity, and specificity compared to the previous approaches.  \nKeywords Lung Cancer, Disease detection, Transformer models, Deep learning, Optimization, Segmentation, And classification  \nLung nodules are small, spherical growths in the lung that appear as areas of increased density on CT images1. These nodules may be caused by conditions such as smoking, which exposes the lungs to extremely dangerous chemicals, or from long-term inhalation offume-like poisons like dust, chemicals, or asbestos2. Early detection and classification of lung nodules in diagnosing lung diseases are very important because early intervention at the right time can be done. Accurately identifying these nodules assists health experts in distinguishing between benign growths and possible malignancies. This can drastically alter a patient’s prognosis and the line of treatment. Hence, early detection enables better management of the lung condition and possibly reduces the burden of the disease, thereby improving survival rates. Lung cancer is the type of cancer originating in the lungs and represents the foremost cause of cancer-related death worldwide. It is usually classified into two broad categories: small cell lung cancer and non-small cell lung cancer3, with quite differing characteristics, modes of treatment, and prognosis. It maybe genetic, environmental exposure, or a result of an individual’s lifestyle choices.  \n1Department of Data Science, AVN Institute of Engineering and Technology, Hyderabad, India. 2Department of Information Technology, Gokaraju Rangaraju Institute of Engineering & Technology, Hyderabad, India. 3Department of CSE-AIML, Vardhaman College of Engineering, Hyderabad, India. 4Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia. 5Department of Information System","cbCaijg4JbSiYKbw","https://ap.wps.com/l/cbCaijg4JbSiYKbw","pdf",5753576,28,"English","# Introduction\n## Role of early diagnosis and imaging\n## Transformer and deep learning for detection\n## Challenges in existing methods\n# Proposed Cancer Nexus Synergy (CanNS) Framework\n## SwiNet for segmentation\n## XLG CancerNet for classification\n## DevLO for parameter fine-tuning\n# Results and Comparative Performance\n## Accuracy, sensitivity, specificity improvements","[{\"question\":\"Why is early lung cancer detection important?\",\"answer\":\"Early diagnosis improves life expectancy by enabling timely interventions and better treatment options. It also supports early differentiation between benign nodules and possible malignancies.\"},{\"question\":\"What limitations in existing deep learning methods motivate CanNS?\",\"answer\":\"Prior work often relies on large annotated data, can overfit, may be computationally heavy, and suffers from weak interpretability. Model stability on real clinical datasets also remains an open challenge.\"},{\"question\":\"How does the CanNS framework work?\",\"answer\":\"CanNS combines Swin-Transformer UNet (SwiNet) for segmentation, Xception-LSTM GAN (XLG) CancerNet for classification, and Devilish Levy Optimization (DevLO) to fine-tune system parameters.\"}]","Enhancing lung cancer detection through integrated deep learning and transformer models | PDF",1790177026,71]