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Dental caries diagnosis in digital radiographs using back-propagation neural network

  • V. GeethaEmail author
  • K. S. Aprameya
  • Dharam M. Hinduja
Research

Abstract

Purpose

An algorithm for diagnostic system with neural network is developed for diagnosis of dental caries in digital radiographs. The diagnostic performance of the designed system is evaluated.

Methods

The diagnostic system comprises of Laplacian filtering, window based adaptive threshold, morphological operations, statistical feature extraction and back-propagation neural network. The back propagation neural network used to classify a tooth surface as normal or having dental caries. The 105 images derived from intra-oral digital radiography, are used to train an artificial neural network with 10-fold cross validation. The caries in these dental radiographs are annotated by a dentist. The performance of the diagnostic algorithm is evaluated and compared with baseline methods.

Results

The system gives an accuracy of 97.1%, false positive (FP) rate of 2.8%, receiver operating characteristic (ROC) area of 0.987 and precision recall curve (PRC) area of 0.987 with learning rate of 0.4, momentum of 0.2 and 500 iterations with single hidden layer with 9 nodes.

Conclusions

This study suggests that dental caries can be predicted more accurately with back-propagation neural network. There is a need for improving the system for classification of caries depth. More improved algorithms and high quantity and high quality datasets may give still better tooth decay detection in clinical dental practice.

Keywords

Computer assisted diagnosis Dental caries Machine learning Back propagation neural network 

Notes

Acknowledgements

Authors would like to thank Dr. R. Gowramma, Principal, S.J.M. Dental College, Chitradurga for providing the datasets used in this research.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Electronics and Communication EngineeringUniversity BDT College of EngineeringDavanagereIndia
  2. 2.Department of Electrical and Electronics EngineeringUniversity BDT College of EngineeringDavanagereIndia
  3. 3.Department of Conservative Dentistry and EndodonticsS.J.M. Dental College & HospitalChitradurgaIndia

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