Mahmoud Y.E.Labib S.S.Mokhtar H.M.O.Faculty of Computer SciencesModern Sciences and Arts UniversityGizaEgypt; Faculty of Computers and InformationCairo UniversityGizaEgypt2020-01-092020-01-0920169.78E+12https://doi.org/10.1109/SAI.2016.7555972PubMed ID :https://t.ly/dOrX6ScopusTeeth 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.EnglishClassificationDiscrete Wavelet TransformHistogram EqualizationArtificial intelligenceClassification (of information)Data miningDiscrete wavelet transformsEqualizersFace recognitionFeature extractionGraphic methodsLearning algorithmsMedian filtersMotion compensationNearest neighbor searchWavelet transformsAverage filterData collectionHistogram equalizationsImage preprocessingK-nearest neighbor classifierMachine learning techniquesProblematic issuesTwo-dimensional discrete wavelet transformLearning systemsConference Paperhttps://doi.org/10.1109/SAI.2016.7555972PubMed ID :