Anomaly detection in panoramic dental x-rays using a hybrid deep learning and machine learning approach

Dhruv Verma, Sunaina Puri, Srikanth Prabhu, Komal Smriti

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publication2020 IEEE Region 10 Conference, TENCON 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages263-268
Number of pages6
ISBN (Electronic)9781728184555
DOIs
Publication statusPublished - 16-11-2020
Event2020 IEEE Region 10 Conference, TENCON 2020 - Virtual, Osaka, Japan
Duration: 16-11-202019-11-2020

Publication series

NameIEEE Region 10 Annual International Conference, Proceedings/TENCON
Volume2020-November
ISSN (Print)2159-3442
ISSN (Electronic)2159-3450

Conference

Conference2020 IEEE Region 10 Conference, TENCON 2020
CountryJapan
CityVirtual, Osaka
Period16-11-2019-11-20

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Electrical and Electronic Engineering

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