TY - GEN
T1 - Anomaly detection in panoramic dental x-rays using a hybrid deep learning and machine learning approach
AU - Verma, Dhruv
AU - Puri, Sunaina
AU - Prabhu, Srikanth
AU - Smriti, Komal
N1 - Publisher Copyright:
© 2020 IEEE.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/11/16
Y1 - 2020/11/16
N2 - Automated anomaly detection in panoramic dental x-rays is a crucial step in streamlining post diagnosis treatment. It can reduce clinical time for a patient and also aid in giving them faster access to medical care. In this paper, we propose a hybrid deep learning and machine learning based approach to detect evident dental caries/periapical infection, altered periodontal bone height, and third molar impactions using panoramic dental radiographs. We use a Convolutional Neural Network as a feature extractor for an input image and use a Support Vector Machine to classify the image as either "Normal"or "Anomalous"based on the extracted features. We compare the performance of this model with the performance of a Convolutional Neural Network and a Support Vector Machine for the same classification task. We also compare our best model with other existing models trained to detect carries and periodontal bone loss. The results obtained with the hybrid deep learning and machine learning approach outperformed the existing methods in the literature.
AB - Automated anomaly detection in panoramic dental x-rays is a crucial step in streamlining post diagnosis treatment. It can reduce clinical time for a patient and also aid in giving them faster access to medical care. In this paper, we propose a hybrid deep learning and machine learning based approach to detect evident dental caries/periapical infection, altered periodontal bone height, and third molar impactions using panoramic dental radiographs. We use a Convolutional Neural Network as a feature extractor for an input image and use a Support Vector Machine to classify the image as either "Normal"or "Anomalous"based on the extracted features. We compare the performance of this model with the performance of a Convolutional Neural Network and a Support Vector Machine for the same classification task. We also compare our best model with other existing models trained to detect carries and periodontal bone loss. The results obtained with the hybrid deep learning and machine learning approach outperformed the existing methods in the literature.
UR - http://www.scopus.com/inward/record.url?scp=85098966497&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85098966497&partnerID=8YFLogxK
U2 - 10.1109/TENCON50793.2020.9293765
DO - 10.1109/TENCON50793.2020.9293765
M3 - Conference contribution
AN - SCOPUS:85098966497
T3 - IEEE Region 10 Annual International Conference, Proceedings/TENCON
SP - 263
EP - 268
BT - 2020 IEEE Region 10 Conference, TENCON 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE Region 10 Conference, TENCON 2020
Y2 - 16 November 2020 through 19 November 2020
ER -