A Deep Learning Approach based on Faster R-CNN for Automatic Detection and Classification of Teeth in Orthopantomogram Radiography Images

Abstract

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.

KEYWORDS:

• Classification
• CNN
• Deep Learning
• Detection
• Dropout
• Faster R-CNN
• Orthopantomogram

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Anuradha Laishram

Anuradha Laishram received her BTech from Visvesvaraya Technological University, India in 2008 and ME in computer science and engineering from Bangalore University, India in 2011. She is currently pursuing her PhD from the National Institute of Technology Manipur (NIT Manipur), India. She is presently working as lecturer at NIT Manipur, India. Her main research interest includes machine learning, deep learning, hybrid intelligent system and medical image processing.

Khelchandra Thongam

Khelchandra Thongam received the MS degree in computer science and engineering from the University of Aizu, Japan in 2007 and his PhD also from the same University. He is currently working as assistant professor at the National Institute of Technology Manipur (NIT Manipur), India. His main research interest includes machine learning, soft computing, hybrid intelligent system, medical image processing, speech processing and mobile robot navigation. Email: [email protected]