ANND: Identification and Prediction of Tooth Decay based on Artificial Neural Network and DenseNet Model

Scientists working in the field of Human health science and technology have recently shown an extraordinary interest in healthcare. Caries is the breakdown of enamel, the protective coating that covers your teeth. It's not only an issue for kids and teenagers, but also for grownups. Plaque, a coating of germs that sticks to your teeth, continually develops. Plaque bacteria secrete acids that wear away dental enamel when you consume sugary foods and beverages. Plaque's stickiness keeps those acids in touch with your teeth, where they may slowly dissolve the enamel. When this happens, holes might develop. Tooth decay, Periodontitis, squamous cell cancers, etc., all poses serious threats to oral health, a complicated area of healthcare. The most important thing is to provide an identifying system to stop these illnesses from spreading to the general population. Scholars can do extensive study on health concerns, including oral health, thanks to the abundance of data available online. Computer-based technology has shown to be able to execute various jobs in limited time and at a lower cost than when utilizing comparable healthcare instruments to conduct the same sort of work, such as dental consultation tools that are already accessible and are very effective. Dental caries prediction is only one area where machine learning has proven useful in oral healthcare. This study used blended model to identifying the tooth decay by highlights the necessity of combining several data sources, also known as multi-modality, to retrieve additional characteristics and produce reliable behaviors, in comparison to the traditional tooth decay prediction previously provided.