Abstract:
Dentists 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.
