Error
  • The authentification system partialy failed, sorry for the inconvenience. Please try again later.
Open Access
Issue
ITM Web Conf.
Volume 79, 2025
International Conference on Knowledge Engineering and Information Systems (KEIS-2025)
Article Number 01025
Number of page(s) 6
DOI https://doi.org/10.1051/itmconf/20257901025
Published online 08 October 2025
  1. International Agency for Research on Cancer, Oral cancer incidence and mortality. World Health Organization (WHO) (2023). https://gco.iarc.fr [Google Scholar]
  2. S. Warnakulasuriya, Global epidemiology of oral and oropharyngeal cancer. Oral Oncol. 45, 309–316 (2009). https://doi.org/10.1016/j.oraloncology.2008.06.002 [Google Scholar]
  3. R.L. Siegel, K.D. Miller, A. Jemal, Cancer statistics, 2023. CA Cancer J. Clin. 73, 17–48 (2023). https://doi.org/10.3322/caac.21763 [Google Scholar]
  4. A. Ali, M.B. Akram, A. Hussain, S.B. Shah, Artificial intelligence in oral cancer detection: A systematic review. IEEE Rev. Biomed. Eng. 14, 123–135 (2021). https://doi.org/10.1109/RBME.2020.3036897 [Google Scholar]
  5. M.M. Rahman, M.S. Bhattacharya, T.H. Kim, Deep learning for oral lesion classification: Challenges and opportunities. Comput. Biol. Med. 128, 104129 (2021). https://doi.org/10.1016/j.compbiomed.2020.104129 [Google Scholar]
  6. L. Zhang, J. Wang, H. Yang, Y. Chen, Z. Liu, Transfer learning in oral cancer diagnostics: A meta-analysis. Artif. Intell. Med. 112, 102003 (2021). https://doi.org/10.1016/j.artmed.2020.101993 [Google Scholar]
  7. P.M. Speight, P.M. Khurram, S. Kujan, Clinical diagnosis of oral potentially malignant disorders. Oral Surg. Oral Med. Oral Pathol. Oral Radiol. 131, 645–654 (2021). https://doi.org/10.1016/j.oooo.2020.08.015 [Google Scholar]
  8. N. Das, A. Hussain, A.A. Ali, R.K. Chandra, CNNbased classification of oral lesions using intraoral images. IEEE J. Biomed. Health Inform. 24, 20212029 (2020). https://doi.org/10.1109/JBHI.2019.2949216 [Google Scholar]
  9. S. Patil, R. Rao, A.A. Ali, Support vector machines for risk stratification of oral pre-cancer. J. Dent. Res. 99, 923–930 (2020). https://doi.org/10.1177/0022034520921650 [Google Scholar]
  10. Y. Chen, L. Zhang, H. Zhao, X. Wang, Z. Xu, Multimodal fusion of OCT and salivary biomarkers for oral cancer detection. Sci. Rep. 12, 6783 (2022). https://doi.org/10.1038/s41598-022-10728-1 [Google Scholar]
  11. J. Wu, F. Zhao, M. Li, K. Wang, Generative adversarial networks for synthetic oral lesion generation. Med. Image Anal. 71, 102075 (2021). https://doi.org/10.1016/j.media.2021.102075 [Google Scholar]
  12. K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, June 26–July 1 (2016), 770–778. [Google Scholar]
  13. L. Breiman, Random forests. Mach. Learn. 45, 5–32 (2001). https://doi.org/10.1023/A:1010933404324 [Google Scholar]
  14. T. Hastie, R. Tibshirani, J. Friedman, The elements of statistical learning: Data mining, inference, and prediction, 2nd ed., Springer, New York (2009). https://doi.org/10.1007/978-0-387-84858-7 [Google Scholar]
  15. A. Esteva, B. Kuprel, R.A. Novoa, J. Ko, S.M. Swetter, H.M. Blau, S. Thrun, Dermatologist-level classification of skin cancer with deep neural networks. Nature 542, 115–118 (2017). https://doi.org/10.1038/nature21056 [CrossRef] [Google Scholar]
  16. U.S. Food and Drug Administration, Artificial intelligence and machine learning in software as a medical device. (2021). https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-and-machine-learning-software-medical-device [Google Scholar]
  17. J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, L. Fei- Fei, ImageNet: A large-scale hierarchical image database, In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, Miami, FL, USA, June 20–25 (2009), 248–255. [Google Scholar]
  18. A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, I. Polosukhin, Attention is all you need, In Advances in Neural Information Processing Systems (NeurIPS), vol. 30, Long Beach, CA, USA, December 4–9 (2017), pp. 5998–6008. https://doi.org/10.48550/arXiv.1706.03762 [Google Scholar]
  19. K. Thamaraiselvi, S. Subramanian, R. Manoharan, Artifact removal in oral cavity images. IEEE J. Biomed. Health Inform. 25, 789–801 (2021). https://doi.org/10.1109/JBHI.2020.2988686 [Google Scholar]
  20. M. Veta, P.J. van Diest, S.M. Willems, H. Wang, A. Madabhushi, A. Cruz-Roa, F. Gonzalez, J. Heng, Deep learning for histopathology image analysis. Med. Image Anal. 58, 101563 (2019). https://doi.org/10.1016/j.media.2019.101563 [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.