Abstract-The teeth are natural self-defense weapon of animals. For human, the pronunciation of language is closely related to the upper and lower front teeth (incisors). Furthermore, the cleanliness of the teeth even has an important influence on daily social activities and status of people. Therefore, when the teeth are diseased or need to be corrected, it becomes particularly vital to conduct a precise classification of different teeth in the oral cavity. In this paper, we will introduce our proposed method, back propagation neural network based on wavelet entropy and Levenberg-Marquardt algorithm, to make a correct classification of the teeth. The total accuracy of our method is 83.83± 2.92%. Our method is better than the state-of-art methods in performance.
To improve the efficiency of stomatology practitioners, this paper proposed a novel teeth type classification method. Our method was based on three successful components: Haar wavelet transform, principal component analysis, and support vector machine. We create a 120-image dataset, with 30 images for incisor, canine, premolar, and molar. The results showed our method achieved an overall classification accuracy of 81.83± 1.79%, better than decision tree and multilayer perceptron methods.
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