{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,3]],"date-time":"2026-11-03T17:56:16Z","timestamp":1793728576447,"version":"4.6.1"},"reference-count":30,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,7,4]],"date-time":"2025-07-04T00:00:00Z","timestamp":1751587200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,7,4]],"date-time":"2025-07-04T00:00:00Z","timestamp":1751587200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"DOI":"10.1038\/s41598-025-07957-9","type":"journal-article","created":{"date-parts":[[2025,7,4]],"date-time":"2025-07-04T05:00:25Z","timestamp":1751605225000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["A tailored deep learning approach for early detection of oral cancer using a 19-layer CNN on clinical lip and tongue images"],"prefix":"10.1038","volume":"15","author":[{"given":"Pinjie","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kambiz","family":"Bagi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,7,4]]},"reference":[{"key":"7957_CR1","doi-asserted-by":"crossref","unstructured":"Prabhakar, S. K. & Rajaguru, H. Performance analysis of linear layer neural networks for oral cancer classification, in 6th ICT International Student Project Conference (ICT-ISPC), 1\u20134. (2017).","DOI":"10.1109\/ICT-ISPC.2017.8075357"},{"key":"7957_CR2","doi-asserted-by":"crossref","unstructured":"Hu, Z. et al. Early stage oral cavity cancer detection: Anisotropic pre-processing and fuzzy C-means segmentation, in IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC), 714\u2013719 (2018).","DOI":"10.1109\/CCWC.2018.8301673"},{"key":"7957_CR3","doi-asserted-by":"crossref","unstructured":"Jubair, F. et al. A novel lightweight deep convolutional neural network for early detection of oral cancer. Oral Dis. (2021).","DOI":"10.1111\/odi.13825"},{"key":"7957_CR4","doi-asserted-by":"crossref","unstructured":"Camalan, S. et al. Convolutional neural network-based clinical predictors of oral dysplasia: class activation map analysis of deep learning results. Cancers 13, 1291 (2021).","DOI":"10.3390\/cancers13061291"},{"key":"7957_CR5","doi-asserted-by":"publisher","first-page":"829","DOI":"10.1007\/s00432-018-02834-7","volume":"145","author":"PR Jeyaraj","year":"2019","unstructured":"Jeyaraj, P. R. & Nadar, E. R. S. Computer-assisted medical image classification for early diagnosis of oral cancer employing deep learning algorithm. J. Cancer Res. Clin. Oncol. 145, 829\u2013837 (2019).","journal-title":"J. Cancer Res. Clin. Oncol."},{"key":"7957_CR6","first-page":"223","volume":"5","author":"AM Ali","year":"2024","unstructured":"Ali, A. M. & Mohammed, M. A. Optimized Cancer subtype classification and clustering using Cat swarm optimization and support vector machine approach for Multi-Omics data. J. Soft Comput. Data Min. 5, 223\u2013244 (2024).","journal-title":"J. Soft Comput. Data Min."},{"key":"7957_CR7","doi-asserted-by":"publisher","first-page":"37","DOI":"10.52866\/ijcsm.2024.05.03.035","volume":"5","author":"MA Mohammed","year":"2024","unstructured":"Mohammed, M. A. & Ali, A. M. Enhanced cancer subclassification using multi-omics clustering and quantum Cat swarm optimization. Iraqi J. Comput. Sci. Math. 5, 37 (2024).","journal-title":"Iraqi J. Comput. Sci. Math."},{"key":"7957_CR8","doi-asserted-by":"publisher","first-page":"114","DOI":"10.59543\/ijmscs.v2i.8703","volume":"2","author":"AM Ali","year":"2024","unstructured":"Ali, A. M. & Mohammed, M. A. A comprehensive review of artificial intelligence approaches in omics data processing: evaluating progress and challenges. Int. J. Math. Stat. Comput. Sci. 2, 114\u2013167 (2024).","journal-title":"Int. J. Math. Stat. Comput. Sci."},{"key":"7957_CR9","unstructured":"Shivam Barot, P. S. Oral Cancer (Lips and Tongue) images. Available: https:\/\/www.kaggle.com\/shivam17299\/oral-cancer-lips-and-tongue-images. (2020)."},{"key":"7957_CR10","doi-asserted-by":"crossref","unstructured":"Cai, X., Li, X., Razmjooy, N. & Ghadimi, N. Breast cancer diagnosis by convolutional neural network and advanced thermal exchange optimization algorithm, Comput. Math. Methods Med. 2021 (2021).","DOI":"10.1155\/2021\/5595180"},{"key":"7957_CR11","doi-asserted-by":"publisher","first-page":"860","DOI":"10.1515\/med-2020-0131","volume":"15","author":"Z Xu","year":"2020","unstructured":"Xu, Z., Sheykhahmad, F. R., Ghadimi, N. & Razmjooy, N. Computer-aided diagnosis of skin cancer based on soft computing techniques. Open Med. 15, 860\u2013871 (2020).","journal-title":"Open Med."},{"key":"7957_CR12","doi-asserted-by":"publisher","first-page":"1144","DOI":"10.1007\/s40815-017-0305-2","volume":"19","author":"N Razmjooy","year":"2017","unstructured":"Razmjooy, N., Ramezani, M. & Ghadimi, N. Imperialist competitive algorithm-based optimization of neuro-fuzzy system parameters for automatic red-eye removal. Int. J. Fuzzy Syst. 19, 1144\u20131156 (2017).","journal-title":"Int. J. Fuzzy Syst."},{"key":"7957_CR13","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1515\/med-2018-0002","volume":"13","author":"FRS Navid","year":"2018","unstructured":"Navid, F. R. S., Razmjooy, N. & Ghadimi A hybrid neural Network \u2013 World cup optimization algorithm for melanoma detection. Open Med. 13, 9\u201316 (2018).","journal-title":"Open Med."},{"key":"7957_CR14","first-page":"313","volume":"31","author":"H Ebrahimian","year":"2018","unstructured":"Ebrahimian, H., Barmayoon, S., Mohammadi, M. & Ghadimi, N. The price prediction for the energy market based on a new method. Econ. Res.. 31, 313\u2013337 (2018).","journal-title":"Econ. Res."},{"key":"7957_CR15","doi-asserted-by":"publisher","first-page":"3478","DOI":"10.1049\/iet-gtd.2019.1625","volume":"14","author":"Z Yuan","year":"2020","unstructured":"Yuan, Z., Wang, W., Wang, H. & Ghadimi, N. Probabilistic decomposition-based security constrained transmission expansion planning incorporating distributed series reactor. IET Gener. Transm. Distrib.. 14, 3478\u20133487 (2020).","journal-title":"IET Generation Transmission Distribution"},{"key":"7957_CR16","doi-asserted-by":"publisher","first-page":"105858","DOI":"10.1016\/j.bspc.2023.105858","volume":"90","author":"Zhang","year":"2024","unstructured":"Zhang et al. A deep learning outline aimed at prompt skin cancer detection utilizing gated recurrent unit networks and improved orca predation algorithm. Biomed. Signal Process. Control. 90, 105858 (2024).","journal-title":"Biomed. Signal Process. Control"},{"key":"7957_CR17","doi-asserted-by":"publisher","first-page":"106024","DOI":"10.1016\/j.bspc.2024.106024","volume":"91","author":"H Liu","year":"2024","unstructured":"Liu, H. & Ghadimi, N. Hybrid convolutional neural network and flexible Dwarf mongoose optimization algorithm for strong kidney stone diagnosis. Biomed. Signal Process. Control. 91, 106024 (2024).","journal-title":"Biomed. Signal Process. Control"},{"key":"7957_CR18","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1007\/s11708-014-0337-3","volume":"9","author":"MT Hagh","year":"2015","unstructured":"Hagh, M. T., Ebrahimian, H. & Ghadimi, N. Hybrid intelligent water drop bundled wavelet neural network to solve the islanding detection by inverter-based DG. Front. Energy. 9, 75\u201390 (2015).","journal-title":"Front. Energy"},{"key":"7957_CR19","doi-asserted-by":"publisher","first-page":"110495","DOI":"10.1016\/j.chaos.2020.110495","volume":"142","author":"E Hussain","year":"2021","unstructured":"Hussain, E. et al. CoroDet: A deep learning based classification for COVID-19 detection using chest X-ray images. Chaos Solitons Fractals. 142, 110495 (2021).","journal-title":"Chaos Solitons Fractals"},{"key":"7957_CR20","doi-asserted-by":"publisher","first-page":"106324","DOI":"10.1016\/j.bspc.2024.106324","volume":"94","author":"M Han","year":"2024","unstructured":"Han, M. et al. Timely detection of skin cancer: an AI-based approach on the basis of the integration of echo state network and adapted seasons optimization algorithm. Biomed. Signal Process. Control. 94, 106324 (2024).","journal-title":"Biomed. Signal Process. Control"},{"key":"7957_CR21","doi-asserted-by":"crossref","unstructured":"Srinivasu, P. N. et al., XAI-driven CatBoost multi-layer perceptron neural network for analyzing breast cancer. Sci. Rep. 14, 28674 (2024).","DOI":"10.1038\/s41598-024-79620-8"},{"key":"7957_CR22","doi-asserted-by":"crossref","unstructured":"Naga Srinivasu, P. et al. Enhancing medical image classification via federated learning and pre-trained model. Egypt. Inform. J., 27, 100530 (2024).","DOI":"10.1016\/j.eij.2024.100530"},{"key":"7957_CR23","doi-asserted-by":"crossref","unstructured":"Kalaiarasi, P. & Rani, P. E. A comparative analysis of AlexNet and GoogLeNet with a simple DCNN for face recognition, in Advances in Smart System Technologies, 655\u2013668. (Springer, 2021).","DOI":"10.1007\/978-981-15-5029-4_54"},{"key":"7957_CR24","doi-asserted-by":"crossref","unstructured":"Ullah, A., Elahi, H., Sun, Z., Khatoon, A. & Ahmad, I. Comparative analysis of alexnet, ResNet18 and squeezenet with diverse modification and arduous implementation. Arab. J. Sci. Eng., pp. 1\u201321, (2021).","DOI":"10.1007\/s13369-021-06182-6"},{"key":"7957_CR25","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1016\/j.ijcard.2020.04.089","volume":"317","author":"Y Zhao","year":"2020","unstructured":"Zhao, Y. et al. Early detection of ST-segment elevated myocardial infarction by artificial intelligence with 12-lead electrocardiogram. Int. J. Cardiol. 317, 223\u2013230 (2020).","journal-title":"Int. J. Cardiol."},{"key":"7957_CR26","doi-asserted-by":"publisher","first-page":"109761","DOI":"10.1016\/j.mehy.2020.109761","volume":"140","author":"F Ucar","year":"2020","unstructured":"Ucar, F. & Korkmaz, D. COVIDiagnosis-Net: deep Bayes-SqueezeNet based diagnosis of the coronavirus disease 2019 (COVID-19) from X-ray images. Med. Hypotheses. 140, 109761 (2020).","journal-title":"Med. Hypotheses"},{"key":"7957_CR27","doi-asserted-by":"publisher","first-page":"944","DOI":"10.1109\/TLA.2021.9451239","volume":"19","author":"E Cort\u00e9s","year":"2021","unstructured":"Cort\u00e9s, E. & S\u00e1nchez, S. Deep learning transfer with AlexNet for chest X-ray COVID-19 recognition. IEEE Lat. Am. Trans. 19, 944\u2013951 (2021).","journal-title":"IEEE Lat. Am. Trans."},{"key":"7957_CR28","doi-asserted-by":"crossref","unstructured":"Yu, X., Wang, S. H., Zhang, X. & Zhang, Y. D. Detection of COVID-19 by GoogLeNet-COD, in International Conference on Intelligent Computing, pp. 499\u2013509. (2020).","DOI":"10.1007\/978-3-030-60799-9_43"},{"key":"7957_CR29","doi-asserted-by":"publisher","first-page":"110713","DOI":"10.1016\/j.chaos.2021.110713","volume":"144","author":"AK Das","year":"2021","unstructured":"Das, A. K., Kalam, S., Kumar, C. & Sinha, D. TLCoV-An automated Covid-19 screening model using transfer learning from chest X-ray images. Chaos Solitons Fractals. 144, 110713 (2021).","journal-title":"Chaos Solitons Fractals"},{"key":"7957_CR30","doi-asserted-by":"crossref","unstructured":"Walvekar, S. & Shinde, D. Detection of COVID-19 from CT images using resnet50, Detection of COVID-19 from CT images using resnet50 (2020).","DOI":"10.2139\/ssrn.3648863"}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-07957-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-07957-9","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-07957-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,4]],"date-time":"2025-07-04T05:00:35Z","timestamp":1751605235000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-07957-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,4]]},"references-count":30,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["7957"],"URL":"https:\/\/doi.org\/10.1038\/s41598-025-07957-9","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,4]]},"assertion":[{"value":"10 February 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 June 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 July 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"23851"}}