Dental Disease Classification Using Image Data And Machine Learning Models
Keywords:
Dental Disease Classification, Machine Learning, Image Embeddings, Neural Network, Multiclass ClassificationAbstract
This study investigates the application of machine learning techniques for the classification of dental diseases based on image data. Two models—Naive Bayes and Neural Network—were evaluated using a publicly available dataset containing 13,839 annotated images across ten dental disease categories. Image embeddings were extracted using the pre-trained Inception v3 model to convert raw images into structured feature vectors. These features were then used to train and evaluate both classifiers using standard performance metrics, including AUC, precision, recall, and F1-score. The results indicate a significant performance gap between the two models. The Neural Network outperformed Naive Bayes across all metrics, achieving an AUC of 0.932 and an F1-score of 0.669, while Naive Bayes performed poorly with near-zero precision and recall. Confusion matrix analysis further highlighted the Neural Network’s superior ability to handle multiclass classification, although it still struggled with underrepresented classes such as Caries 2, Caries 3, and Caries 4. These findings suggest that deep learning-based approaches, when combined with robust image embeddings, are more effective for dental disease classification tasks and offer strong potential for supporting automated diagnostic systems in dentistry.
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