Automatic Classification of Oral Pathologies Using Orthopantomogram Radiography Images Based on Convolutional Neural Network

Abstract

An attempt has been made to device a robust method to classify different oral pathologies using Orthopantomogram (OPG) images based on Convolutional Neural Network (CNN). This system will provide a novel approach for the classification of types of teeth (viz., incisors and molar teeth) and also some underlying oral anomalies such as fixed partial denture (cap) and impacted teeth. To this end, various image preprocessing techniques are performed. The input OPG images are resized, pixels are scaled and erroneous data are excluded. The proposed algorithm is implemented using CNN with Dropout and the fully connected layer has been trained using hybrid GA-BP learning. Using the Dropout regularization technique, over fitting has been avoided and thereby making the network to correctly classify the objects. The CNN has been implemented with different convolutional layers and the highest accuracy of 97.92% has been obtained with two convolutional layers.