{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T16:05:31Z","timestamp":1784217931530,"version":"3.55.0"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,7,16]]},"DOI":"10.7759\/cureus.112795","type":"journal-article","created":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T15:34:23Z","timestamp":1784216063000},"source":"Crossref","is-referenced-by-count":0,"title":["Deep Learning-Based Oral Cancer Detection Using Clinical Images"],"prefix":"10.7759","author":[{"given":"Arman","family":"Amin","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mark A","family":"Bachir","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Akshay J","family":"Reddy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ayush","family":"Vyas","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ashesh","family":"Ashesh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jacob","family":"Webster","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rakesh","family":"Patel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21660","article-title":"Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries","volume":"71","author":"Sung H","year":"2021","unstructured":"Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021, 71:209-49. 10.3322\/caac.21660","journal-title":"CA Cancer J Clin"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21763","article-title":"Cancer statistics, 2023","volume":"73","author":"Siegel RL","year":"2023","unstructured":"Siegel RL, Miller KD, Wagle NS, Jemal A. Cancer statistics, 2023. CA Cancer J Clin. 2023, 73:17-48. 10.3322\/caac.21763","journal-title":"CA Cancer J Clin"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.oraloncology.2008.06.002","article-title":"Global epidemiology of oral and oropharyngeal cancer","volume":"45","author":"Warnakulasuriya S","year":"2009","unstructured":"Warnakulasuriya S. Global epidemiology of oral and oropharyngeal cancer. Oral Oncol. 2009, 45:309-16. 10.1016\/j.oraloncology.2008.06.002","journal-title":"Oral Oncol"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1111\/j.1749-4486.2012.02464.x","article-title":"Impact of delay in diagnosis on survival to head and neck carcinomas: a systematic review with meta-analysis","volume":"37","author":"Seoane J","year":"2012","unstructured":"Seoane J, Takkouche B, Varela-Centelles P, Tom\u00e1s I, Seoane-Romero JM. Impact of delay in diagnosis on survival to head and neck carcinomas: a systematic review with meta-analysis. Clin Otolaryngol. 2012, 37:99-106. 10.1111\/j.1749-4486.2012.02464.x","journal-title":"Clin Otolaryngol"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.3399\/bjgp18X700205","article-title":"Diagnosis and referral delays in primary care for oral squamous cell cancer: a systematic review","volume":"69","author":"Grafton-Clarke C","year":"2019","unstructured":"Grafton-Clarke C, Chen KW, Wilcock J. Diagnosis and referral delays in primary care for oral squamous cell cancer: a systematic review. Br J Gen Pract. 2019, 69:e112-26. 10.3399\/bjgp18X700205","journal-title":"Br J Gen Pract"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.4317\/medoral.18756","article-title":"The relevance of uniform reporting in oral leukoplakia: definition, certainty factor and staging based on experience with 275 patients","volume":"18","author":"Brouns ER","year":"2013","unstructured":"Brouns ER, Baart JA, Bloemena E, Karagozoglu H, van der Waal I. The relevance of uniform reporting in oral leukoplakia: definition, certainty factor and staging based on experience with 275 patients. Med Oral Patol Oral Cir Bucal. 2013, 18:e19-26. 10.4317\/medoral.18756","journal-title":"Med Oral Patol Oral Cir Bucal"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1111\/odi.13704","article-title":"Oral potentially malignant disorders: a consensus report from an international seminar on nomenclature and classification, convened by the WHO Collaborating Centre for Oral Cancer","volume":"27","author":"Warnakulasuriya S","year":"2021","unstructured":"Warnakulasuriya S, Kujan O, Aguirre-Urizar JM, et al.. Oral potentially malignant disorders: a consensus report from an international seminar on nomenclature and classification, convened by the WHO Collaborating Centre for Oral Cancer. Oral Dis. 2021, 27:1862-80. 10.1111\/odi.13704","journal-title":"Oral Dis"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1111\/jop.13227","article-title":"Automatic classification and detection of oral cancer in photographic images using deep learning algorithms","volume":"50","author":"Warin K","year":"2021","unstructured":"Warin K, Limprasert W, Suebnukarn S, Jinaporntham S, Jantana P. Automatic classification and detection of oral cancer in photographic images using deep learning algorithms. J Oral Pathol Med. 2021, 50:911-8. 10.1111\/jop.13227","journal-title":"J Oral Pathol Med"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1117\/1.JBO.26.8.086007","article-title":"Automatic detection of oral cancer in smartphone-based images using deep learning for early diagnosis","volume":"26","author":"Lin H","year":"2021","unstructured":"Lin H, Chen H, Weng L, Shao J, Lin J. Automatic detection of oral cancer in smartphone-based images using deep learning for early diagnosis. J Biomed Opt. 2021, 26:10.1117\/1.JBO.26.8.086007","journal-title":"J Biomed Opt"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1089\/tmj.2016.0128","article-title":"Mobile phone imaging in low resource settings for early detection of oral cancer and concordance with clinical oral examination","volume":"23","author":"Haron N","year":"2017","unstructured":"Haron N, Zain RB, Nabillah WM, et al.. Mobile phone imaging in low resource settings for early detection of oral cancer and concordance with clinical oral examination. Telemed J E Health. 2017, 23:192-9. 10.1089\/tmj.2016.0128","journal-title":"Telemed J E Health"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3010180","article-title":"Automated detection and classification of oral lesions using deep learning for early detection of oral cancer","volume":"8","author":"Welikala RA","year":"2020","unstructured":"Welikala RA, Remagnino P, Lim JH, et al.. Automated detection and classification of oral lesions using deep learning for early detection of oral cancer. IEEE Access. 2020, 8:132677-93. 10.1109\/ACCESS.2020.3010180","journal-title":"IEEE Access"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1016\/j.eclinm.2020.100558","article-title":"A deep learning algorithm for detection of oral cavity squamous cell carcinoma from photographic images: a retrospective study","volume":"27","author":"Fu Q","year":"2020","unstructured":"Fu Q, Chen Y, Li Z, et al.. A deep learning algorithm for detection of oral cavity squamous cell carcinoma from photographic images: a retrospective study. EClinicalMedicine. 2020, 27:100558. 10.1016\/j.eclinm.2020.100558","journal-title":"EClinicalMedicine"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.3390\/cancers15164120","article-title":"AI-assisted screening of oral potentially malignant disorders using smartphone-based photographic images","volume":"15","author":"Talwar V","year":"2023","unstructured":"Talwar V, Singh P, Mukhia N, et al.. AI-assisted screening of oral potentially malignant disorders using smartphone-based photographic images. Cancers (Basel). 2023, 15:10.3390\/cancers15164120","journal-title":"Cancers (Basel)"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1038\/nature21056","article-title":"Dermatologist-level classification of skin cancer with deep neural networks","volume":"542","author":"Esteva A","year":"2017","unstructured":"Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, Thrun S. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017, 542:115-8. 10.1038\/nature21056","journal-title":"Nature"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun Y","year":"2015","unstructured":"LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015, 521:436-44. 10.1038\/nature14539","journal-title":"Nature"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2017.07.005","article-title":"A survey on deep learning in medical image analysis","volume":"42","author":"Litjens G","year":"2017","unstructured":"Litjens G, Kooi T, Bejnordi BE, et al.. A survey on deep learning in medical image analysis. Med Image Anal. 2017, 42:60-88. 10.1016\/j.media.2017.07.005","journal-title":"Med Image Anal"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2016.2535302","article-title":"Convolutional neural networks for medical image analysis: full training or fine tuning?","volume":"35","author":"Tajbakhsh N","year":"2016","unstructured":"Tajbakhsh N, Shin JY, Gurudu SR, Hurst RT, Kendall CB, Gotway MB, Jianming Liang. Convolutional neural networks for medical image analysis: full training or fine tuning?. IEEE Trans Med Imaging. 2016, 35:1299-312. 10.1109\/TMI.2016.2535302","journal-title":"IEEE Trans Med Imaging"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1905.11946","article-title":"EfficientNet: rethinking model scaling for convolutional neural networks","volume":"97","author":"Tan M","year":"2019","unstructured":"Tan M, Le QV. EfficientNet: rethinking model scaling for convolutional neural networks. Proc Int Conf Mach Learn. 2019, 97:6105-14. 10.48550\/arXiv.1905.11946","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.3390\/cancers13112766","article-title":"Automated detection and classification of oral lesions using deep learning to detect oral potentially malignant disorders","volume":"13","author":"Tanriver G","year":"2021","unstructured":"Tanriver G, Soluk Tekkesin M, Ergen O. Automated detection and classification of oral lesions using deep learning to detect oral potentially malignant disorders. Cancers (Basel). 2021, 13:10.3390\/cancers13112766","journal-title":"Cancers (Basel)"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.3390\/cancers13061291","article-title":"Convolutional neural network-based clinical predictors of oral dysplasia: class activation map analysis of deep learning results","volume":"13","author":"Camalan S","year":"2021","unstructured":"Camalan S, Mahmood H, Binol H, et al.. Convolutional neural network-based clinical predictors of oral dysplasia: class activation map analysis of deep learning results. Cancers (Basel). 2021, 13:10.3390\/cancers13061291","journal-title":"Cancers (Basel)"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0273508","article-title":"AI-based analysis of oral lesions using novel deep convolutional neural networks for early detection of oral cancer","volume":"17","author":"Warin K","year":"2022","unstructured":"Warin K, Limprasert W, Suebnukarn S, Jinaporntham S, Jantana P, Vicharueang S. AI-based analysis of oral lesions using novel deep convolutional neural networks for early detection of oral cancer. PLoS One. 2022, 17:e0273508. 10.1371\/journal.pone.0273508","journal-title":"PLoS One"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-023-29204-9","article-title":"Detection of oral squamous cell carcinoma in clinical photographs using a vision transformer","volume":"13","author":"Fl\u00fcgge T","year":"2023","unstructured":"Fl\u00fcgge T, Gaudin R, Sabatakakis A, Tr\u00f6ltzsch D, Heiland M, van Nistelrooij N, Vinayahalingam S. Detection of oral squamous cell carcinoma in clinical photographs using a vision transformer. Sci Rep. 2023, 13:2296. 10.1038\/s41598-023-29204-9","journal-title":"Sci Rep"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1111\/odi.13825","article-title":"A novel lightweight deep convolutional neural network for early detection of oral cancer","volume":"28","author":"Jubair F","year":"2022","unstructured":"Jubair F, Al-Karadsheh O, Malamos D, Al Mahdi S, Saad Y, Hassona Y. A novel lightweight deep convolutional neural network for early detection of oral cancer. Oral Dis. 2022, 28:1123-30. 10.1111\/odi.13825","journal-title":"Oral Dis"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics11061004","article-title":"Application and performance of artificial intelligence technology in oral cancer diagnosis and prediction of prognosis: a systematic review","volume":"11","author":"Khanagar SB","year":"2021","unstructured":"Khanagar SB, Naik S, Al Kheraif AA, et al.. Application and performance of artificial intelligence technology in oral cancer diagnosis and prediction of prognosis: a systematic review. Diagnostics (Basel). 2021, 11:10.3390\/diagnostics11061004","journal-title":"Diagnostics (Basel)"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.74","article-title":"Grad-CAM: visual explanations from deep networks via gradient-based localization","volume":"618","author":"Selvaraju RR","year":"2017","unstructured":"Selvaraju RR, Cogswell M, Das A, et al.. Grad-CAM: visual explanations from deep networks via gradient-based localization. Proc IEEE Int Conf Comput Vis. 2017, 618:26. 10.1109\/ICCV.2017.74","journal-title":"Proc IEEE Int Conf Comput Vis"},{"key":"ref26","unstructured":"Oral cancer dataset. (2026). Accessed. July 14, 2026: https:\/\/www.kaggle.com\/datasets\/zaidpy\/oral-cancer-dataset."},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848","article-title":"ImageNet: A large-scale hierarchical image database","volume":"248","author":"Deng J","year":"2009","unstructured":"Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. ImageNet: A large-scale hierarchical image database. Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit. 2009, 248:55. 10.1109\/CVPR.2009.5206848","journal-title":"Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1412.6980","article-title":"Adam: a method for stochastic optimization","volume":"3","author":"Kingma DP","year":"2015","unstructured":"Kingma DP, Ba J. Adam: a method for stochastic optimization. Int Conf Learn Represent. 2015, 3:10.48550\/arXiv.1412.6980","journal-title":"Int Conf Learn Represent"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1603.04467","article-title":"TensorFlow: large-scale machine learning on heterogeneous distributed systems [Preprint]","author":"Abadi M","year":"2016","unstructured":"Abadi M, Agarwal A, Barham P, et al.. TensorFlow: large-scale machine learning on heterogeneous distributed systems [Preprint]. arXiv. 2016, 10.48550\/arXiv.1603.04467","journal-title":"arXiv"},{"key":"ref30","unstructured":"Keras. (2015). Accessed. July 14, 2026: https:\/\/github.com\/fchollet\/keras."},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-020-2649-2","article-title":"Array programming with NumPy","volume":"585","author":"Harris CR","year":"2020","unstructured":"Harris CR, Millman KJ, van der Walt SJ, et al.. Array programming with NumPy. Nature. 2020, 585:357-62. 10.1038\/s41586-020-2649-2","journal-title":"Nature"},{"key":"ref32","article-title":"Scikit-learn: machine learning in Python","volume":"12","author":"Pedregosa F","year":"2011","unstructured":"Pedregosa F, Varoquaux G, Gramfort A, et al.. Scikit-learn: machine learning in Python. J Mach Learn Res. 2011, 12:2825-30.","journal-title":"J Mach Learn Res"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/MCSE.2007.55","article-title":"Matplotlib: a 2D graphics environment","volume":"9","author":"Hunter JD","year":"2007","unstructured":"Hunter JD. Matplotlib: a 2D graphics environment. Comput Sci Eng. 2007, 9:90-5. 10.1109\/MCSE.2007.55","journal-title":"Comput Sci Eng"},{"key":"ref34","article-title":"The OpenCV Library","volume":"25","author":"Bradski G","year":"2000","unstructured":"Bradski G. The OpenCV Library. Dr Dobbs J Softw Tools. 2000, 25:120-5.","journal-title":"Dr Dobbs J Softw Tools"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-017-12320-8","article-title":"Automatic classification of cancerous tissue in laserendomicroscopy images of the oral cavity using deep learning","volume":"7","author":"Aubreville M","year":"2017","unstructured":"Aubreville M, Knipfer C, Oetter N, et al.. Automatic classification of cancerous tissue in laserendomicroscopy images of the oral cavity using deep learning. Sci Rep. 2017, 7:11979. 10.1038\/s41598-017-12320-8","journal-title":"Sci Rep"}],"container-title":["Cureus"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.cureus.com\/articles\/513577-deep-learning-based-oral-cancer-detection-using-clinical-images","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T15:34:28Z","timestamp":1784216068000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.cureus.com\/articles\/513577-deep-learning-based-oral-cancer-detection-using-clinical-images"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,16]]},"references-count":35,"URL":"https:\/\/doi.org\/10.7759\/cureus.112795","relation":{},"ISSN":["2168-8184"],"issn-type":[{"value":"2168-8184","type":"print"}],"subject":[],"published":{"date-parts":[[2026,7,16]]}}}