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This paper presents a new method for teeth and anomalies detection and classification using Faster Region Convolutional Neural Network with Deep Learning. Four classes of teeth and two classes of teeth anomalies are used for the classification by using Orthopantomogram radiography images as input. Using the Regional Proposal Network (RPN) and Object Detection Network (ODN), the detection of teeth objects has been made possible which replaces the manual segmentation of each individual tooth from the set of teeth signals thereby making the whole system more efficient. Overfitting is avoided by using the Dropout technique and thus improves the accuracy of the system. The model is trained and tested with the input samples and also compared with the ground truth and it achieves an accuracy of 92% for detection and 99.72% for classification. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]