ABSTRACT
Oral cancer has a high incidence and fatality rate, making it a leading cancer killer. Death rates from oral cancer have remained high over the previous few decades despite progress in oncology therapy. Most people diagnosed with oral cancer will not receive adequate care on time. Notably, they were leading to low survival rates in the countryside. 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 cnn network to assess oral cancer diagnosis. This project is developed in matlab.
PROJECT OUTPUT
PROJECT DEMO VIDEO
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