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Traditional machine learning models often struggle with limited clinical generalizability. This study develops a two-stage deep learning framework that uses transfer learning to segment lung tumor regions from CT scans and classify images as cancerous or noncancerous, addressing gaps seen in conventional 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Bahareh Shateri-Amiri1 | Amirhossein Hajialiasgary Najafabadi2 | Sina Garazhian3  | Hanieh Radkhah4 | Diar Zooravar5  | Sahar Mansouri5 | Roya Aghazadeh5 | Mohammadreza Bordbar5 | Shirin Raiszadeh5  \n1Department of Internal Medicine, School of Medicine, Rasool Akram Medical Complex, Iran University of Medical Sciences, Tehran, Iran | 2Research Group Quantitative and Computational Biology, Max-Planck-Institute for Multidisciplinary Sciences, University of Göttingen, Göttingen,  \nGermany | 3Research Group of Quantitative and Systems Biology, Max-Planck-Institute for Multidisciplinary Sciences (MPI-NAT), University of Göttingen, Göttingen, Germany | 4Department of Internal Medicine, School of Medicine, Sina Hospital, Tehran University of Medical Sciences, Tehran, Iran | 5School of Medicine, Iran University of Medical Sciences, Tehran, Iran  \nCorrespondence: Bahareh Shateri-Amiri ([shateribahare@gmail.com](shateribahare@gmail.com)) | Diar Zooravar ([diarzooravar@gmail.com](diarzooravar@gmail.com))  \nReceived: 17 November 2024 | Revised: 21 April 2025 | Accepted: 19 May 2025  \nFunding: The authors received no specific funding for this work.  \nKeywords: classification metrics | CT scan | deep learning | lung cancer | machine learning | ResNet50 | transfer learning | tumor segmentation  \nABSTRACT  \nBackground and Aims: Lung cancer remains a leading cause of cancer-related deaths worldwide, with early detection critical for improving prognosis. Traditional machine learning (ML) models have shown limited generalizability in clinical settings. This study proposes a deep learning-based approach using transfer learning to accurately segment lung tumor regions from CT scansand classify images as cancerous or noncancerous, aiming to overcome the limitations of conventional ML models.  \nMethods: We developed a two-stage model utilizing a ResNet50 backbone within a U-Net architecture for lesion segmentation, followed by a multi-layer perceptron (MLP) for binary classification. The model was trained on publicly available CT scan datasets and evaluated on an independent clinical dataset from Hazrat Rasool Hospital, Iran. Training employed binary crossentropy and Dice loss functions. Data augmentation, dropout, and regularization were used to enhance model generalizability and prevent overfitting.  \nResults: The model achieved 94% accuracy on the real-world clinical test set. Evaluation metrics, including F1 score, Matthews correlation coefficient (MCC), Cohen's kappa, and Dice index, confirmed the model's robustness and diagnostic reliability. In comparison, traditional ML models performed poorly on external test data despite high training accuracy, highlighting a significant generalization gap.  \nConclusion: This research presents a reliable deep learning framework for lung cancer detection that outperforms traditional ML approaches on external validation. The results demonstrate its potential for clinical deployment. Future work will focus on prospective validation, interpretability techniques, and integration into hospital workflows to support real-time decision making and regulatory compliance.  \n\n| Abbreviations: AI, artificial intelligence; CNN, convolutional neural network; CT, computed tomography; ML, machine learning; MLP, multi-layer perceptron; ROC, receiver operating characteristic. |\n| --- |\n| This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.\u003Cbr>© 2025 The Author(s). Cancer Reports published by Wiley Periodicals LLC. |\n\nCancer Reports, 2025; 8:e70240 1 of 8  \n[https://doi.org/10.1002/cnr2.70240](https://doi.org/10.1002/cnr2.70240)  \n1 | Introduction  \nLung cancer remains one of the most pressing global health","cbCailzmSObcFD8K","https://ap.wps.com/l/cbCailzmSObcFD8K","pdf",744132,"English","# Abstract\n## Background and Aims\n## Methods\n## Results\n## Conclusion\n# Introduction","[{\"question\":\"What problem does the study address in lung cancer care?\",\"answer\":\"The study targets limited early detection performance and the poor clinical generalizability of traditional machine learning models for lung cancer on real-world data.\"},{\"question\":\"How does the proposed model work?\",\"answer\":\"It uses a two-stage design: a ResNet50 backbone within a U-Net for lesion segmentation, followed by a multilayer perceptron for binary classification of cancerous versus noncancerous images.\"},{\"question\":\"How well did the model perform compared with traditional ML methods?\",\"answer\":\"The framework achieved 94% accuracy on an independent clinical test set, while traditional ML models performed poorly on external test data despite high training accuracy, showing a generalization gap.\"}]","Lung Cancer Management: Revolutionizing Patient Outcomes Through Machine Learning and Artificial Intelligence | PDF",1790164243]