Abstract
In recent days, oral cancer cases are significantly increasing due to increase in tobacco consumption, along with the combination of consumption of alcohol, poor oral hygiene, and human papilloma virus (HPV) infection. Early detection of this kind of cancers is preventive, or else, it may leads to premature deaths. 50% of cases are detected in advanced stages. For the above reasons, it is important to develop the new model to detect the oral cavity cancer in early stage from the digital data and image processing techniques. The research in detection of oral cancer is highly active from the twentieth century. In this paper, the detection of oral cancer with the fusion model of CNN + RNN is proposed. The proposed model outperforms the state-of-art techniques in detection of oral cancer with 82% of accuracy. The obtained result is analyzed with systematic approach, and assured diagnosis is ensured the diagnosis of the oral cancer we attempt to near future. The intention of the proposed method is to improve the detection accuracy in the early diagnosis of oral cavity cancers.
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Sucharitha, S.T., Kannan, I., Varun Kumar, K.A. (2023). Early Detection and Diagnosis of Oral Cancer Using Fusioned Deep Neural Network. In: Rao, B.N.K., Balasubramanian, R., Wang, SJ., Nayak, R. (eds) Intelligent Computing and Applications. Smart Innovation, Systems and Technologies, vol 315. Springer, Singapore. https://doi.org/10.1007/978-981-19-4162-7_27
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DOI: https://doi.org/10.1007/978-981-19-4162-7_27
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