  Analysis of dental radiographs is an important part of the diagnostic process
in daily clinical practice. Interpretation by an expert includes teeth
detection and numbering. In this project, a novel solution based on adaptive
histogram equalization and convolution neural network (CNN) is proposed, which
automatically performs the task for dental x-rays. In order to improve the
detection accuracy, we propose three pre-processing techniques to supplement
the baseline CNN based on some prior domain knowledge. Firstly, image
sharpening and median filtering are used to remove impulse noise, and the edge
is enhanced to some extent. Next, adaptive histogram equalization is used to
overcome the problem of excessive amplification noise of HE. Finally, a
multi-CNN hybrid model is proposed to classify six different locations of
dental slices. The results showed that the accuracy and specificity of the test
set exceeded 90\%, and the AUC reached 0.97. In addition, four dentists were
invited to manually annotate the test data set (independently) and then compare
it with the labels obtained by our proposed algorithm. The results show that
our method can effectively identify the X-ray location of teeth.
