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Optimized classification of dental implants using convolutional neural networks and pre-trained models with

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  • 1Department of Computer Engineering, Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran. lashaki@tabrizu.ac.ir.

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Summary

Data augmentation and preprocessing significantly improve dental implant classification accuracy. Convolutional Random Forest (CRF) and Convolutional Neural Networks (CNN) were top classifiers, while VGG16 excelled among pre-trained models.

Keywords:
CNNClassificationDental implants radiography imagesPre-trained models

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Area of Science:

  • Medical Imaging
  • Computer Vision
  • Dental Technology

Background:

  • Accurate dental implant state classification is crucial for diagnosis and treatment planning.
  • Radiography images are commonly used but require robust analysis methods.
  • Existing classification methods may benefit from advanced preprocessing and augmentation.

Purpose of the Study:

  • To evaluate the performance of various classifiers and pre-trained models for dental implant state classification.
  • To assess the impact of data augmentation and preprocessing on classification accuracy.
  • To identify optimal models for classifying dental implant states from radiography images.

Main Methods:

  • A dataset of 511 periapical images was augmented to 5110 using rotation, flipping, and scaling.
  • Images underwent preprocessing: resizing, sharpening, noise reduction, contrast enhancement, and implant-specific masking.
  • Classifiers included Convolutional Neural Networks (CNN), Convolutional Support Vector Machine (CSVM), Convolutional Decision Tree (CDT), and Convolutional Random Forest (CRF).
  • Pre-trained models (VGG16, ResNet50, Xception) were utilized for feature extraction.
  • Performance was evaluated using accuracy, precision, recall, F1 score, and ROC AUC with fivefold cross-validation.

Main Results:

  • Convolutional Random Forest (CRF) showed high performance for ITI with Bego implants (accuracy 0.8966).
  • Convolutional Neural Networks (CNN) achieved the best results for Bicon with Bego implants (accuracy 0.9533).
  • VGG16, a pre-trained model, demonstrated superior performance for Bicon vs. ITI classification (accuracy 0.9865) when using preprocessed data.
  • Data augmentation and preprocessing significantly enhanced the performance of all evaluated classifiers.

Conclusions:

  • Data augmentation and preprocessing are vital for improving the robustness and accuracy of dental implant classification.
  • CRF and CNN emerged as highly effective classifiers for this task.
  • VGG16 proved to be the most effective pre-trained model, underscoring the value of transfer learning in this domain.
  • The study confirms the importance of advanced image processing techniques in dental radiography analysis.