Eid Mahmoud, YasmineSafwat Labib, SohaM. O. Mokhtar, Hoda2020-02-152020-02-152015[1] Henriksen K, Battles JB, Keyes MA, Grady ML, editors. (August 2008). Advances in patient safety: New directions and alternative approaches. AHRQ Publication [2] Weiser TG, Regenbogen SE, Thompson KD, Haynes AB, Lipsitz SR, Berry WR.(12 Jul 2008) An estimation978-1-941968-26-0https://doi.org/10.13140/RG.2.1.1343.4967https://t.ly/33Bg5MSA Google ScholarDentists used to diagnose teeth periapical lesion according to patient’s dental 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, sometimes reaching definitive diagnosis before starting the treatment 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 segmentation and expectation maximization 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.enOctober University for University of Image Segmentation; Expectation Maximization; Histogram Equalization; ClassificationBook chapterhttps://doi.org/10.13140/RG.2.1.1343.4967