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This study proposes an explainable deep learning framework combining pre-trained networks (VGG16, ResNet50, InceptionV3) to improve lung cancer detection by enhancing feature extraction, addressing imbalance, and validating performance on the IQ-OTH/NCCD dataset.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/explainable-lung-cancer-classification-with-ensemble-transfer-learning-of-vgg16-resnet50-and-inceptionv3-using-grad-cam/341538/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/explainable-lung-cancer-classification-with-ensemble-transfer-learning-of-vgg16-resnet50-and-inceptionv3-using-grad-cam/341538.png","ImageObject",300,407,{"name":92,"@type":93},"Oliver","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-23","2026-09-22",true,{"@type":102,"interactionType":103,"userInteractionCount":39},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What problem does the study address in lung cancer diagnosis?","Question",{"text":112,"@type":113},"It addresses limitations of manual interpretation and conventional machine learning, including subjectivity and insufficient handling of complex image features for reliable classification of benign versus malignant cases.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"Which models are combined in the proposed approach?",{"text":117,"@type":113},"The framework integrates pre-trained VGG16, ResNet50, and InceptionV3 within a unified system to leverage complementary feature extraction strengths.",{"name":119,"@type":110,"acceptedAnswer":120},"How is class imbalance handled during training?",{"text":121,"@type":113},"The method applies SMOTE and Gaussian Blur to mitigate imbalance and improve learning on underrepresented classes.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},341538,1790831574,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":39,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":56,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":143,"read_time":144},8796095461610,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c","Kumaran S etal. BMC Medical Imaging (2024) 24:176  \n[https://doi.org/10.1186/s12880-024-01345-x](https://doi.org/10.1186/s12880-024-01345-x)  \nBMC Medical Imaging  \nRESEARCH Open Access  \nExplainable lung cancer classification with ensemble transfer learning of VGG16, Resnet50 and InceptionV3 using grad-cam  \nYogesh Kumaran S 1, J. Jospin Jeya2, Mahesh T R 1*, Surbhi Bhatia Khan3, Saeed Alzahrani4 and Mohammed Alojail4  \nAbstract  \nMedical imaging stands as a critical component in diagnosing various diseases, where traditional methods often rely on manual interpretation and conventional machine learning techniques. These approaches, while effective, come with inherent limitations such as subjectivity in interpretation and constraints in handling complex image features. This research paper proposes an integrated deep learning approach utilizing pre-trained models—VGG16, ResNet50, and InceptionV3—combined within a unified framework to improve diagnostic accuracy in medical imaging. The method focuses on lung cancer detection using images resized and converted to a uniform format to optimize performance and ensure consistency across datasets. Our proposed model leverages the strengths of each pre-trained network, achieving a high degree of feature extraction and robustness by freezing the early convolutional layers and fine-tuning the deeper layers. Additionally, techniques like SMOTE and Gaussian Blurare applied to address class imbalance, enhancing model training on underrepresented classes. The model’s performance was validated on the IQ-OTH/NCCD lung cancer dataset, which was collected from the Iraq-Oncology Teaching Hospital/National Center for Cancer Diseases over a period of three months in fall 2019. The proposed model achieved an accuracy of 98. 18%, with precision and recall rates notably high across all classes. This improvement highlights the potential of integrated deep learning systems in medical diagnostics, providing a more accurate, reliable, and efficient means of disease detection.  \nKeywords Deep learning, Medical imaging, Lung cancer detection, VGG16, ResNet50, InceptionV3, SMOTE, Image classification, Feature extraction, Diagnostic accuracy  \n*Correspondence:  \nMahesh T R  \n[trmahesh.1978@gmail.com](trmahesh.1978@gmail.com)  \n1Department of Computer Science & Engineering, Faculty of Engineering and Technology, JAIN (Deemed-to-be University), Bengaluru  \n562112, India  \n2Department of Computer Science and Engineering, SRM Institute of Science and Technology, Ramapuram, Chennai, India  \n3School of science, engineering and environment, University of Salford, Manchester, UK  \n4Management Information System Department, College of Business Administration, King Saud University, Riyadh, Saudi Arabia  \nIntroduction  \nMedical imaging is a pivotal tool for diagnosing a plethora of diseases, with lung cancer being one of the most critical due to its high mortality rate worldwide. Lung cancer, characterized by the uncontrolled growth of abnormal cells in one or both lungs, typically presents through various radiographic manifestations such as nodules, masses, or unusual opacities. The early detection and accurate classification of these manifestations into benign (non-cancerous) or malignant (cancerous) categories are crucial for effective treatment planning and improved patient outcomes [1, 2].  \n© The Author(s) 2024, corrected publication 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creati","cbCaipLNj4msZYfy","https://ap.wps.com/l/cbCaipLNj4msZYfy","pdf",3671233,"English","# Abstract\n# Introduction","[{\"question\":\"What problem does the study address in lung cancer diagnosis?\",\"answer\":\"It addresses limitations of manual interpretation and conventional machine learning, including subjectivity and insufficient handling of complex image features for reliable classification of benign versus malignant cases.\"},{\"question\":\"Which models are combined in the proposed approach?\",\"answer\":\"The framework integrates pre-trained VGG16, ResNet50, and InceptionV3 within a unified system to leverage complementary feature extraction strengths.\"},{\"question\":\"How is class imbalance handled during training?\",\"answer\":\"The method applies SMOTE and Gaussian Blur to mitigate imbalance and improve learning on underrepresented classes.\"}]","Explainable lung cancer classification with ensemble transfer learning of VGG16, Resnet50 and InceptionV3 using grad-cam | PDF",1790042440,48]