Abstract
The most common disease on the planet is dental caries, also known as cavities. Almost everyone has had this condition at some point in their lives. Early identication of dental caries can considerably reduce the risk of serious damage to teeth in people who have dental disease. As medical imaging becomes more efcient and faster to use, clinical applications are having a greater impact on patient care. Recently, there has been a lot of interest in machine learning approaches for categorizing and analyzing image data. In this study, we describe a new strategy for locating and identifying dental caries from X-ray photos as a dataset and using associative classication as a classication method. This technique incorporates both classication and correlation. The numerical discrimination approach is also used in the strategy. This is the rst study to employ association-based classications to determine dental cavities and root canal treatment positions. This method was tested on real data from hundreds of patients and found to be very good at nding unexpected damage to teeth.
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