{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T15:18:31Z","timestamp":1786547911455,"version":"build-2736575974"},"reference-count":28,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T00:00:00Z","timestamp":1785456000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T00:00:00Z","timestamp":1786492800000},"content-version":"vor","delay-in-days":12,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"Symbiosis International"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"DOI":"10.1038\/s41598-026-64739-7","type":"journal-article","created":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T11:26:01Z","timestamp":1785497161000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Deep learning driven early detection of lung cancer from CT images using transfer learning approaches"],"prefix":"10.1038","volume":"16","author":[{"given":"Vishwas V.","family":"Patange","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jagadish B.","family":"Jadhav","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sanjay L.","family":"Nalbalwar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Priyanka V.","family":"Deshmukh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,31]]},"reference":[{"key":"64739_CR1","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","volume":"42","author":"G Litjens","year":"2017","unstructured":"Litjens, G. et al. A survey on deep learning in medical image analysis. Med. Image. Anal. 42, 60\u201388 (2017).","journal-title":"Med. Image. Anal."},{"issue":"6","key":"64739_CR2","doi-asserted-by":"publisher","first-page":"954","DOI":"10.1038\/s41591-019-0447-x","volume":"25","author":"D Ardila","year":"2019","unstructured":"Ardila, D. et al. End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nat. Med. 25(6), 954\u2013961 (2019).","journal-title":"Nat. Med."},{"key":"64739_CR3","unstructured":"Mahimkar, A. & IQ-OTH\/NCCD - Lung Cancer Dataset, Kaggle, https:\/\/www.kaggle.com\/datasets\/adityamahimkar\/iqothnccd-lung-cancer-dataset (Accessed 20 July 2025) (2019)."},{"key":"64739_CR4","unstructured":"Syed, A. A. et al. Comparative analysis of learning algorithms for lung cancer identification. Indian J. Sci. Technol. (2018)."},{"key":"64739_CR5","doi-asserted-by":"crossref","unstructured":"Dash, J. K. et al. Multi-classifier framework for lung tissue classification. In IEEE Students\u2019 Technology Symposium (TechSym), 264\u2013269 (2014).","DOI":"10.1109\/TechSym.2014.6808058"},{"key":"64739_CR6","doi-asserted-by":"crossref","unstructured":"G\u00fcnaydin, O. et al. Comparison of lung cancer detection algorithms. In Scientific Meeting on Electrical-Electronics and Biomedical Engineering and Computer Science (EBBT), 1\u20134 (2019).","DOI":"10.1109\/EBBT.2019.8741826"},{"key":"64739_CR7","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1016\/j.procs.2017.12.016","volume":"125","author":"S Makaju","year":"2018","unstructured":"Makaju, S. et al. Lung cancer detection using CT scan images. Procedia Comput. Sci. 125, 107\u2013114 (2018).","journal-title":"Procedia Comput. Sci."},{"key":"64739_CR8","doi-asserted-by":"publisher","first-page":"90380","DOI":"10.1109\/ACCESS.2020.2993872","volume":"8","author":"Q Zhang","year":"2020","unstructured":"Zhang, Q. & Kong, X. J. I. A. Design of automatic lung nodule detection system based on multi-scene deep learning framework. IEEE Access 8, 90380\u201390389 (2020).","journal-title":"IEEE Access"},{"key":"64739_CR9","doi-asserted-by":"crossref","unstructured":"Ausawalaithong, W. et al. Automatic lung cancer prediction from chest X-ray images using the deep learning approach. In Biomedical Engineering International Conference (BMEiCON), 1\u20135 (2018).","DOI":"10.1109\/BMEiCON.2018.8609997"},{"key":"64739_CR10","doi-asserted-by":"publisher","first-page":"208","DOI":"10.1016\/j.inffus.2020.10.004","volume":"67","author":"S Wang","year":"2021","unstructured":"Wang, S. et al. Covid-19 classification by FGCNet with deep feature fusion from graph convolutional network and convolutional neural network. Inform. Fusion. 67, 208\u2013229 (2021).","journal-title":"Inform. Fusion"},{"key":"64739_CR11","doi-asserted-by":"publisher","DOI":"10.1007\/s12652-021-02998-0","author":"S Wang","year":"2021","unstructured":"Wang, S. et al. Explainable diagnosis of secondary pulmonary tuberculosis by graph rank-based average pooling neural network. J. Ambient Intell. Humaniz. Comput. https:\/\/doi.org\/10.1007\/s12652-021-02998-0 (2021).","journal-title":"J. Ambient Intell. Humaniz. Comput."},{"key":"64739_CR12","doi-asserted-by":"publisher","first-page":"53687","DOI":"10.1109\/ACCESS.2021.3071057","volume":"9","author":"M Li","year":"2021","unstructured":"Li, M. et al. Research on the auxiliary classification and diagnosis of lung cancer subtypes based on histopathological images. IEEE Access. 9, 53687\u201353707 (2021).","journal-title":"IEEE Access."},{"issue":"2","key":"64739_CR13","doi-asserted-by":"publisher","first-page":"1099","DOI":"10.1177\/09287329241296438","volume":"33","author":"N Banupriya","year":"2025","unstructured":"Banupriya, N. & Sethukarasi, T. Deep learning-driven multi-omics sequential diagnosis with Hybrid-OmniSeq: Unraveling breast cancer complexity. Technol. Health Care. 33(2), 1099\u20131120 (2025).","journal-title":"Technol. Health Care"},{"issue":"1","key":"64739_CR14","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-025-11750-0","volume":"38","author":"N Banupriya","year":"2026","unstructured":"Banupriya, N. & Sethukarasi, T. Gene-biomarker data-driven deep learning model with M-DMAN for ER, PR, HER2 enrichment and breast cancer association prediction. Neural Comput. Appl. 38(1), 2 (2026).","journal-title":"Neural Comput. Appl."},{"key":"64739_CR15","doi-asserted-by":"publisher","first-page":"86400","DOI":"10.1109\/ACCESS.2020.2992645","volume":"8","author":"H Yu","year":"2020","unstructured":"Yu, H. et al. Deep learning assisted prediction of lung cancer on computed tomography images using the adaptive hierarchical heuristic mathematical model. IEEE Access. 8, 86400\u201386410 (2020).","journal-title":"IEEE Access."},{"issue":"16","key":"64739_CR16","doi-asserted-by":"publisher","first-page":"2617","DOI":"10.3390\/diagnostics13162617","volume":"13","author":"MA Thanoon","year":"2023","unstructured":"Thanoon, M. A. et al. A review of deep learning techniques for lung cancer screening and diagnosis based on CT images. Diagnostics 13(16), 2617 (2023).","journal-title":"Diagnostics"},{"key":"64739_CR17","doi-asserted-by":"crossref","unstructured":"Mamun, M. et al. Lcdctcnn: Lung cancer diagnosis of CT scan images using CNN based model. In International Conference on Signal Processing and Integrated Networks (SPIN), 205\u2013212 (2023).","DOI":"10.1109\/SPIN57001.2023.10116075"},{"key":"64739_CR18","unstructured":"Altarawneh, M. Lung cancer detection using image processing techniques. Leonardo Electron. J. Practices Technol.\u00a0(2012)."},{"key":"64739_CR19","doi-asserted-by":"publisher","first-page":"374","DOI":"10.1016\/j.future.2018.10.009","volume":"92","author":"SK Lakshmanaprabu","year":"2019","unstructured":"Lakshmanaprabu, S. K. et al. Optimal deep learning model for classification of lung cancer on CT images. Future Generation Comput. Syst. 92, 374\u2013382 (2019).","journal-title":"Future Generation Comput. Syst."},{"issue":"4","key":"64739_CR20","first-page":"1098","volume":"10","author":"MCS Mez","year":"2022","unstructured":"Mez, M. C. S. et al. Lung cancer detection by hybrid learning method applying SMOTE technique. Gazi Univ. J. Sci. Part. C. 10(4), 1098\u20131110 (2022).","journal-title":"Gazi Univ. J. Sci. Part. C"},{"issue":"74","key":"64739_CR21","first-page":"126","volume":"8","author":"SM Ashhar","year":"2021","unstructured":"Ashhar, S. M. et al. Comparison of deep learning convolutional neural network (CNN) architectures for CT lung cancer classification. Int. J. Adv. Technol. Eng. Explor. 8(74), 126\u2013132 (2021).","journal-title":"Int. J. Adv. Technol. Eng. Explor."},{"key":"64739_CR22","doi-asserted-by":"crossref","unstructured":"Cengil, E. & Cinar, A. A deep learning based approach to lung cancer identification. In International Conference on Artificial Intelligence and Data Processing (IDAP), 1\u20135 (2018).","DOI":"10.1109\/IDAP.2018.8620723"},{"key":"64739_CR23","unstructured":"Tan, M. & Le, Q. V. EfficientNet: Rethinking model scaling for convolutional neural networks. In International Conference on Machine Learning (ICML), 6105\u20136114 (2019)."},{"key":"64739_CR24","doi-asserted-by":"crossref","unstructured":"He, K. et al. Deep residual learning for image recognition. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 770\u2013778 (2016).","DOI":"10.1109\/CVPR.2016.90"},{"key":"64739_CR25","doi-asserted-by":"crossref","unstructured":"Huang, G. et al. Densely connected convolutional networks. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 4700\u20134708 (2017).","DOI":"10.1109\/CVPR.2017.243"},{"key":"64739_CR26","unstructured":"Dosovitskiy, A. et al. An image is worth 16\u00d716 words: Transformers for image recognition at scale. In International Conference on Learning Representations (ICLR), (2021)."},{"issue":"12","key":"64739_CR27","first-page":"3312","volume":"41","author":"Y Chen","year":"2022","unstructured":"Chen, Y. et al. TransUNet: Transformers make strong encoders for medical image segmentation. IEEE Trans. Med. Imaging. 41(12), 3312\u20133323 (2022).","journal-title":"IEEE Trans. Med. Imaging"},{"key":"64739_CR28","doi-asserted-by":"publisher","first-page":"22629","DOI":"10.1007\/s11042-017-5023-0","volume":"77","author":"YD Zhang","year":"2018","unstructured":"Zhang, Y. D. et al. Smart pathological brain detection by synthetic minority oversampling technique, extreme learning machine, and Jaya algorithm. Multimedia Tools Appl. 77, 22629\u201322648 (2018).","journal-title":"Multimedia Tools Appl."}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-64739-7","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-64739-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-64739-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T14:19:15Z","timestamp":1786544355000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-64739-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,31]]},"references-count":28,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["64739"],"URL":"https:\/\/doi.org\/10.1038\/s41598-026-64739-7","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,31]]},"assertion":[{"value":"17 April 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 July 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 July 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","label":"Competing interests","group":{"name":"EthicsHeading","label":"Declarations"}}],"article-number":"25122"}}