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Fusion: Practice and Applications

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https://doi.org/10.54216/FPA

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2692-4048ISSN (Online) 2770-0070ISSN (Print)
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Fusion: Practice and Applications

Volume 14 , Issue 1 , PP: 293-308, 2024 | Cite this article as | XML | Html | PDF | Full Length Article

Early Diagnosis of Oral Cancer Using Image Processing and Artificial Intelligence

Eman Shawky Mira 1 * , Ahmed M. Saaduddin Sapri 2 , Rowaa F. Aljehanı 3 , Bayan S. Jambı 4 , Taseer Bashir 5 , El-Sayed M. El-Kenawy 6 , Mohamed Saber 7

  • 1 Diagnosis & Oral Radiology Department, Faculty of Dentistry , Mansoura University, Mansoura 35516, Egypt - (emanshawky@mans.edu.eg)
  • 2 Oral and Maxillofacial Surgery Department, Faculty of Dentistry, Mansoura University, Mansoura 35516, Egypt - (ahmedsaaduddin@mans.edu.eg)
  • 3 Dentistry Program, Batterjee Medical College, Jeddah, Saudi Arabia - (Ro2a_aljehani@hotmail.com)
  • 4 Dentistry Program, Batterjee Medical College, Jeddah, Saudi Arabia - (bayanja11@gmail.com)
  • 5 Department of Oral medicine and radiology, Batterjee Dental College, Jeddah, Saudi Arabia - (taseermds2013@gmail.com)
  • 6 Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura 35111, Egypt - (skenawy@ieee.org)
  • 7 Electronics and Communications Engineering Department, Faculty of Engineering, Delta University for Science and Technology, Gamasa City 11152, Egypt - (Mohamed.Saber@deltauniv.edu.eg)

    • Doi: https://doi.org/10.54216/FPA.140122

    Received: July 24, 2023 Revised: October 27, 2023 Accepted: December 19, 2023
    Abstract

    There has yet to be a comprehensive investigation on enhancing the diagnostic accuracy of oral disease using handheld smartphone photographic photos. To overcome the difficulties associated with the automatic detection of oral illnesses, we describe an approach based on smartphone image diagnosis powered by a deep learning algorithm. The centered rule method of image capture was offered as a quick and easy way to get high-quality pictures of the mouth. A resampling method was proposed to mitigate the influence of image variability from handheld smartphone cameras, and a medium-sized oral dataset with five types of disorders was developed based on this approach. Finally, we introduce a recently developed deep-learning network to assess oral cancer diagnosis. On 455 test images, the proposed technique showed an impressive 83.0% sensitivity, 96.6% specificity, 84.3% accuracy, and 83.6% F1. The proposed "center positioning" method was about 8% higher than a simulated "random positioning" method; the resampling process had an additional 6% performance improvement. The performance of a deep learning algorithm for detecting oral cancer can be enhanced by capturing oral photos centered on the lesion. Primary oral cancer diagnosis using smartphone-based images with deep learning offers promising potential.

    Keywords :

    Deep learning , Smartphone-based imaging , Image collection , Oral cancer diagnosis , Oral potentially malignant disorders.

    References

    [1]     H. Sung et al., Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countrie, CA Cancer J. Clin., 71(3), 209-249, 2021.

    [2]     A. Jemal et al., Annual report to the nation on the status of cancer, featuring survival,  JNCI-J. Natl. Cancer Inst. 109(9), 1975-2014, 2017.

    [3]     P. Mathur et al., Cancer statistics, 2020: report from national cancer registry programme, India,  JCO Global Oncol. 6(6), 1063–1075, 2020.

    [4]     W. Sungwalee et al., Comparing survival of oral cancer patients before and after launching of the universal coverage scheme in Thailand, Asian Pac. J. Cancer Prev., 17(7), 3541-3544, 2016.

    [5]     T. S. Dantas et al., Influence of educational level, stage, and histological type on survival of oral cancer in a Brazilian population: a retrospective study of 10 years observation, Medicine (Baltimore), 95(3), e2314, 2016.

    [6]     D. Chakraborty, N. Chandrasekaran, and A. Mukherjee, Advances in oral cancer detection, Adv. Clin. Chem. 91, 181-200, 2019.

    [7]     D. K. Zanoni et al., Survival outcomes after treatment of cancer of the oral cavity (1985-2015), Oral Oncol. 90, 115-121, 2019.

    [8]     S. Choi and J. N. Myers, Molecular pathogenesis of oral squamous cell carcinoma: implications for therapy, J. Dent. Res. 87(1), 14-32, 2008.

    [9]     B. Ayaz et al., A clinico-pathological study of oral cancers. Biomedica,  27, 29-32, 2011.

    [10]   P. M. Speight, S. A. Khurram, and O. Kujan, Oral potentially malignant disorders: risk of progression to malignancy, Oral Surg. Oral Med. Oral Pathol. Oral Radiol., 125(6), 612-627 2018.

    [11]    G. Yardimci et al., Precancerous lesions of oral mucosa, World J. Clin. Cases, 2(12), 866-872 2014.

    [12]   B. W. Neville and T. A. Day, Oral cancer and precancerous lesions, CA-Cancer J. Clin., 52(4), 195-215, 2002.

    [13]   F. Dost et al., A retrospective analysis of clinical features of oral malignant and potentially malignant disorders with and without oral epithelial dysplasia, Oral Surg. Oral Med. Oral Pathol. Oral Radiol., 116 (6), 725-733, 2013.

    [14]   M. Aubreville et al., Automatic classification of cancerous tissue in laser endo microscopy images of the oral cavity using deep learning, Sci. Rep., 7(1), 11979, 2017.

    [15]    B. Song et al., Automatic classification of dual-modalilty, smartphone-based oral dysplasia and malignancy images using deep learning, Biomed. Opt. Express 9(11), 5318–5329, 2018.

    [16]   S. Camalan et al., Convolutional neural network-based clinical predictors of oral dysplasia: class activation map analysis of deep learning results, Cancers 13(6), 1291, 2021..

    [17]   R. A. Welikala et al., Fine-tuning deep learning architectures for early detection of oral cancer. in Mathematical and Computational Oncology, 25-31, 2020.

    [18]   Poh CF, Ng S, Berean KW, Williams PM, Rosin MP, and Zhang L , Biopsy and histopathologic diagnosis of oral premalignant and malignant lesions. Journal of the Canadian Dental Association, 74(3), 283-288, 2008.

    [19]    Branes L, Eveson J, and Reichart P, Tumors of the oral cavity and oropharynx. Pathol Genet, 67, 177-179, 2005.

    [20]   Lumerman H, Freedman P, and Kerpel S, Oral epithelial dysplasia and the development of invasive squamous cell carcinoma. Oral Surgery, Oral Medicine, Oral Pathology, Oral Radiology and Endodontics, 1995; 79(3): 321-329.

    [21]    Hayat MA. Principles and techniques of electron microscopy, Biological applications. 1981: Edward Arnold. Buajeeb W, Poomsawat S, Punyasingh J, and Sanguansin S, Expression of p16 in oral cancer and premalignant lesions. Journal of oral pathology & medicine, 38(1), 104-108, 2009.

    [22]    D. Jia et al., ImageNet: a large-scale hierarchical image database, in Proc. Conf. Comput. Vis. Pattern Recognit., 248-255, 2009.

    [23]   I. J. Goodfellow et al., Generative adversarial networks, in Adv. Neural Inf. Proc. Sys., (NIPS), 2672-2680, 2014.

    [24]    T. Y. Lin et al., Focal loss for dense object detection, in Proc. Conf. Comput. Vis. Pattern Recognit.,  2980–2988, 2017.

    [25]    C. Elkan, The foundations of cost-sensitive learning, in Proc. Joint Conf. Artif. Intell., 973-978 2001.

    [26]    K. Sun et al., High-resolution representations for labeling pixels and regions, arXiv:1904.04514 2019.

    [27]    J. Wang et al., Deep high-resolution representation learning for visual recognition, in IEEE Trans. Pattern Anal. Mach. Intell., 1–1, 2020.

    [28]    K. He et al., Deep residual learning for image recognition,” in Proc. Conf. Comput. Vis. Pattern Recognit., 770-778, 2016.

    [29]    K. Simonyan and A. Zisserman, Very deep convolutional networks for large-scale image recognition, in Int. Conf. Learn. Repr., (ICLR), 2014.

    [30]    G. Huang et al., Densely connected convolutional networks, in Proc. Conf. Comput. Vis. Pattern Recognit., 4700–4708, 2017.

    Cite This Article As :
    Shawky, Eman. , M., Ahmed. , F., Rowaa. , S., Bayan. , Bashir, Taseer. , M., El-Sayed. , Saber, Mohamed. Early Diagnosis of Oral Cancer Using Image Processing and Artificial Intelligence. Fusion: Practice and Applications, vol. , no. , 2024, pp. 293-308. DOI: https://doi.org/10.54216/FPA.140122
    Shawky, E. M., A. F., R. S., B. Bashir, T. M., E. Saber, M. (2024). Early Diagnosis of Oral Cancer Using Image Processing and Artificial Intelligence. Fusion: Practice and Applications, (), 293-308. DOI: https://doi.org/10.54216/FPA.140122
    Shawky, Eman. M., Ahmed. F., Rowaa. S., Bayan. Bashir, Taseer. M., El-Sayed. Saber, Mohamed. Early Diagnosis of Oral Cancer Using Image Processing and Artificial Intelligence. Fusion: Practice and Applications , no. (2024): 293-308. DOI: https://doi.org/10.54216/FPA.140122
    Shawky, E. , M., A. , F., R. , S., B. , Bashir, T. , M., E. , Saber, M. (2024) . Early Diagnosis of Oral Cancer Using Image Processing and Artificial Intelligence. Fusion: Practice and Applications , () , 293-308 . DOI: https://doi.org/10.54216/FPA.140122
    Shawky E. , M. A. , F. R. , S. B. , Bashir T. , M. E. , Saber M. [2024]. Early Diagnosis of Oral Cancer Using Image Processing and Artificial Intelligence. Fusion: Practice and Applications. (): 293-308. DOI: https://doi.org/10.54216/FPA.140122
    Shawky, E. M., A. F., R. S., B. Bashir, T. M., E. Saber, M. "Early Diagnosis of Oral Cancer Using Image Processing and Artificial Intelligence," Fusion: Practice and Applications, vol. , no. , pp. 293-308, 2024. DOI: https://doi.org/10.54216/FPA.140122

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