Affiliation:
1. HiTech Dental College and Hospital, Bhubaneswar, Odisha, India.
2. GITAM (Deemed to be University), Visakhapatnam, India.
3. IIIT Bhubaneswar, Odisha, India.
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.
Subject
General Medicine,Organic Chemistry,Drug Discovery,Pharmacology,General Medicine,Law,Demography,Geochemistry and Petrology,Cell Biology,Genetics,Molecular Biology,Applied Microbiology and Biotechnology,Molecular Medicine,Immunology,Microbiology,Agricultural and Biological Sciences (miscellaneous),Anatomy,Physical and Theoretical Chemistry,Biomedical Engineering,Medicine (miscellaneous),Bioengineering,General Neuroscience,Nutrition and Dietetics,Medicine (miscellaneous),Pharmacology,Oncology
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