Study on Detecting the Teeth and Classifying the Teeth Structure using Machine Learning and CNN

A dental professional deciphers several dental diagnostic modalities in order to detect anomalies, changes in tooth structure, or tooth-related issues. Dentists assess several dental X-ray diagnostic methods in order to detect problems with anomalies in teeth or structural alterations to teeth. In automated dental identification, distinguishing teeth into four categories (molars, premolars, canines, and incisors) has emerged as a difficult task. To find missing teeth and wide spaces between teeth, one must first understand the fundamentals of tooth anatomy before concentrating on tooth identification and tooth groups. A set of teeth from both the mandibles is shown in the dentistry panoramic imagery. Finding anomalies in developmental stages is hard; the same goes for classifying cavities, dental gaps, broken teeth, and data patterns that don't match expected behavior. The deep convolutional neural network is one of the most effective tools for resolving complex problems in a wide range of domains, including diagnosis in medicine, the illustration recognition, and classification. A dentistry panoramic image is made up of the teeth in the upper and lower jaws together. Dental professionals play a crucial role in the process of automatically classifying dental panoramic images into distinct tooth types, among which are canines, incisors, premolars, and molars. This is a challenging task. This study reviews the ways based on CNN, discusses the problems associated with classifying teeth and anomalies, and tabulates the benefits and drawbacks of each method. Regarding the process of numbering and categorizing, we can use the CNN technique to find anomalies and get the best level of accuracy.