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

Anuradha Laishram; Khelchandra Thongam

doi:10.9781/ijimai.2021.10.009

Authors

Anuradha Laishram National Institute of Technology Manipur
Khelchandra Thongam National Institute of Technology Manipur

DOI:

https://doi.org/10.9781/ijimai.2021.10.009

Keywords:

Classification, Convolutional Neural Network (CNN), Dropout, Data Pre-processing, Orthopantomogram Radiography Images

Supporting Agencies

We are immensely thankful to NIT Manipur for providing a platform to do the research work and Cosmo Dental Clinic Manipur for providing the dataset.

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.

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