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Hyperparameter Tuning and Automatic Image Augmentation for Deep Learning-Based Angle Classification on Intraoral
José Eduardo Cejudo Grano de Oro1, Petra Julia Koch2, Joachim Krois1
1Department of Oral Diagnostics, Digital Health and Health Services Research, Charité Center for Oral Health Sciences CC3, Charité-Universitätsmedizin Berlin (Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin), Aßmannshauser Straße 4-6, 14197 Berlin, Germany.
Optimizing deep learning hyperparameters and using automatic image augmentation significantly improved orthodontic photograph classification accuracy for Angle dental classes. These methods enhance diagnostic capabilities in orthodontics.
Area of Science:
- Artificial Intelligence in Dentistry
- Medical Image Analysis
- Orthodontic Diagnostics
Background:
- Accurate classification of dental sagittal occlusion along Angle classes is crucial for orthodontic treatment planning.
- Deep learning models offer potential for automating this classification from intraoral photographs.
- Hyperparameter tuning and data augmentation are key to optimizing deep learning performance.
Purpose of the Study:
- To evaluate the impact of hyperparameter tuning and automatic image augmentation on deep learning models for classifying orthodontic Angle classes.
- To identify optimal learning rates and batch sizes for ResNet architectures in this context.
- To assess the performance improvements gained from automatic augmentation and model explainability.
Main Methods:
- Trained ResNet architectures on a dataset of 605 Angle class I, 1038 class II, and 408 class III orthodontic images.
- Systematically tuned learning rates and batch sizes, comparing models with and without automatic image augmentation.
- Employed 10-fold cross-validation for performance evaluation and GradCAM for model explainability.
Main Results:
- Optimal hyperparameter combinations (learning rate ~1-3 × 10-6, batch size 8) achieved accuracies of 0.63-0.64 and F1-scores of 0.61-0.62.
- Automatic image augmentation improved all performance metrics by 5-10%.
- GradCAM analysis confirmed that models utilized features relevant to human classification, with most misclassifications occurring between Angle classes I and II.
Conclusions:
- Hyperparameter selection critically influences deep learning model performance in orthodontic image classification.
- Automatic image augmentation provides substantial performance gains, enhancing diagnostic accuracy.
- The developed deep learning models demonstrate capability in classifying dental sagittal occlusion from intraoral photos.
