Abstract
About one-fifth of all oral cancer cases reported globally are from India. The low-income groups in India are affected most due to the wide exposure to risk factors such as tobacco chewing and insufficient access to early diagnostic tools. Visual examination and histological study are the standard for oral cancer detection. This paper proposes the idea of using Autofluorescence-based imaging techniques to detect and classify oral cancer using AI algorithms. Various features of the images along with medical history, age, gender, and tobacco usage are considered as inputs to the proposed Mobilenet classification architecture.
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