Teeth periapical lesion prediction using machine learning techniques

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Date

2016

Journal Title

Journal ISSN

Volume Title

Type

Conference Paper

Publisher

Institute of Electrical and Electronics Engineers Inc.

Series Info

Proceedings of 2016 SAI Computing Conference, SAI 2016

Abstract

Teeth Periapical lesion is used to be diagnosed by dentists according to patient's x-ray. But most of the time there were a problematic issue to reach a definitive diagnosis. It takes too much time, case and chief complaint history needed, many tests and tools are needed and sometimes taking too many radiographs is required. Even though, before starting the treatment sometimes reaching definitive diagnosis is difficult. Therefore, the objective of this research is to predict whether the patient has teeth periapical lesion or not and its type using machine learning techniques. The proposed system consists of four main steps: Data collection, image preprocessing using median and average filters for removing noise and Histogram equalization for image enhancement, feature extraction using two dimensional discrete wavelet transform algorithm, and finally machine learning (classification) using Feed Forward Neural Networks and K-Nearest Neighbor Classifier. It has been concluded from the results that the K-Nearest Neighbor Classifier performs better than Feed Forward Neural Network on our real database. � 2016 IEEE.

Description

Scopus

Keywords

Classification, Discrete Wavelet Transform, Histogram Equalization, Artificial intelligence, Classification (of information), Data mining, Discrete wavelet transforms, Equalizers, Face recognition, Feature extraction, Graphic methods, Learning algorithms, Median filters, Motion compensation, Nearest neighbor search, Wavelet transforms, Average filter, Data collection, Histogram equalizations, Image preprocessing, K-nearest neighbor classifier, Machine learning techniques, Problematic issues, Two-dimensional discrete wavelet transform, Learning systems

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