Abstract
Oral cancer is one of the most common cancers worldwide and more prevalent in India due to a variety of carcinogenic irritants, both inhaled and chewed, leading to more than 50,000 deaths annually. It not only causes loss of precious lives but morbidity due to oral cancers and the exuberant cost of treatment is a financial burden to patients and the state. When identified early, in benign, oral potentially malignant, or even in early stages of malignant disease, the 5-year survival rate of more than 80% can be expected. As early detection is the most important key to improved prognosis, the development of Artificial intelligence-based image recognition at Primary and Secondary healthcare centers is of paramount importance. In this study, we have explored the potential of Deep learning-based application to identify and segregate normal, benign, oral potentially malignant lesions and malignant lesions. This application offers great potential for a non-invasive technique for the early detection of oral neoplastic lesions.
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Appendix
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Images of Normal Oral Mucosa (a), Premalignant lesions [Erythroplakia (b), Leukoplakia (c ), Melanoplakia (d), OSMF (e)] and Malignant lesions (f,g,h).
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Chawla, P., Roy, P. (2023). Role of Artificial Intelligence in the Screening of Neoplastic Oral Lesions. In: Shaw, R.N., Paprzycki, M., Ghosh, A. (eds) Advanced Communication and Intelligent Systems. ICACIS 2022. Communications in Computer and Information Science, vol 1749. Springer, Cham. https://doi.org/10.1007/978-3-031-25088-0_69
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DOI: https://doi.org/10.1007/978-3-031-25088-0_69
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