Dental X –Ray Images for Automated Detection of Caries Classes Using Deep Learning Techniques

Abstract

Purpose: Dental caries can emerge anywhere in the mouth particularly in the interior of the cheeks and the gums. Some of the indications are patches on the inner lining of the mouth, along with bleeding, toothache, numbness and an unusual red and white staining. Hence, it is important to predict the presence of cavity at an early stage. The currently available manual method is inefficient and hence we provide an advanced method by using the deep learning concepts.

Materials and Methods: In this work, different types of algorithms such as Res Net, Deeper Google Net and mini VGG Net are to be used to predict the class of cavity at an early stage.

Results: A comparison between the accuracy of three different algorithms is given in this paper. Thus, by using efficient deep learning algorithms, it will be able to predict the presence of cavity and the class of cavity at an early stage and take necessary steps to overcome it.

Conclusion: In this work, a comparison between three different algorithms is given and proved that the efficient algorithm is the inception algorithm among the other algorithms and achieve an accuracy of about 98%, which is suitable for use in hospitals.