Teeth Classification Based on Haar Wavelet Transform and Support Vector Machine

Liu, Fangyuan; Li, Zhi; Quinn, Wagner

doi:10.2991/csece-18.2018.53

Proceedings of the 2018 International Conference on Computer Science, Electronics and Communication Engineering (CSECE 2018) 2018

DOI: 10.2991/csece-18.2018.53

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Teeth Classification Based on Haar Wavelet Transform and Support Vector Machine

Fangyuan Liu¹,

Zhi Li²,

Wagner Quinn³

Abstract: To improve the efficiency of stomatology practitioners, this paper proposed a novel teeth type classification method. Our method was based on three successful components: Haar wavelet transform, principal component analysis, and support vector machine. We create a 120-image dataset, with 30 images for incisor, canine, premolar, and molar. The results showed our method achieved an overall classification accuracy of 81.83± 1.79%, better than decision tree and multilayer perceptron methods.

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2022

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Texture-Based Neural Network Model for Biometric Dental Applications

Saleh

Nozaki

Matsumura

et al. 2022

JPM

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Background: The aim is to classify dentition using a novel texture-based automated convolutional neural network (CNN) for forensic and prosthetic applications. Methods: Natural human teeth (n = 600) were classified, cleaned, and inspected for exclusion criteria. The teeth were scanned with an intraoral scanner and identified using a texture-based CNN in three steps. First, through preprocessing, teeth images were segmented by extracting the front-facing region of the teeth. Then, texture features were extracted from the segmented teeth images using the discrete wavelet transform (DWT) method. Finally, deep learning-based enhanced CNN models were used to identify these images. Several experiments were conducted using five different CNN models with various batch sizes and epochs, with and without augmented data. Results: Based on experiments with five different CNN models, the highest accuracy achieved was 0.8 and the precision was 0.8 with a loss value of 0.9, a batch size of 32, and 250 epochs. A comparison of deep learning models with different parameters showed varied accuracy between the different classes of teeth. Conclusion: The accuracy of the point-based CNN method was promising. This texture-identification method will pave the way for many forensic and prosthodontic applications and will potentially help improve the precision of dental biometrics.

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Texture-Based Neural Network Model for Biometric Dental Applications

Saleh

Nozaki

Matsumura

et al. 2022

JPM

View full text Add to dashboard Cite

show abstract

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Teeth Classification Based on Haar Wavelet Transform and Support Vector Machine

Cited by 1 publication

References 38 publications

Texture-Based Neural Network Model for Biometric Dental Applications

Texture-Based Neural Network Model for Biometric Dental Applications

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