Abstract: Over 3.9 billion people worldwide get affected by dental cavities. Barriers such as dento phobia, limited dentist availability, and lack of dental insurance prevent millions from receiving dental health care. To address this, an Artificial Intelligence system will be developed that can detects cavity presence on photographs. For preventing further damage of teeth, it is necessary to detect cavity as soon as possible. This is particularly significant as it addresses issues related to accessibility, affordability, and convenience in the domain of dental healthcare. By using the widespread availability of smartphones, this innovative approach has the potential to change oral health assessments, reaching a greater number of people and promoting proactive dental care. The "Oral Cavity Detection" project aims to revolutionize dental diagnostics through the application of deep learning techniques. Using the TensorFlow API for object detection, this system will operate seamlessly on a web-based platform, providing a user-friendly interface for the detection and prediction of oral cavities within images of teeth. For training of this cavity detection model, the custom dataset will be required. Comprehensive analysis of this study reveals positive results that can be improved in the
future and can be implemented on a commercial scale.
Keywords: Healthcare, Artificial Intelligence, TensorFlow, web-based platform, User friendly
Cite:
Vaibhav Bhagat, Dr. Vaishali Phalke, Soham Kate, Yogesh Algude, Tanmay Bhosale,Ms. Punam Desai, "Early Cavity Detection Using Image Processing Approach", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 13, no. 3, 2024, Crossref https://doi.org/10.17148/IJARCCE.2024.13321.
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DOI:
10.17148/IJARCCE.2024.13321
