Optimized Dental Caries Prediction Using Image Based Deep Learning

Abstract

Dental caries is a common oral disease that progresses rapidly if not detected early. This paper proposes an optimized deep learning framework for automatic caries prediction from scanned dental images. The system integrates Convolutional Neural Networks (CNN) with preprocessing, data augmentation, and transfer learning to enhance detection accuracy. Experiments performed on annotated image datasets demonstrate superior precision, recall, and F1-score compared with traditional manual examination. The model’s robustness enables reliable detection under varying lighting and imaging conditions. A simple graphical interface is developed for clinicians to upload scanned images and receive real-time predictions, promoting early diagnosis and preventive dental care. The backbone of this system is a convolutional neural network (CNN) model that processes and analyzes dental images. By using image-based deep learning, the system eliminates the subjectivity and errors that may occur in human diagnosis. CNN models can identify subtle patterns and minute variations in the dental structure that may not be visible to the naked eye, especially during the early stages of caries development. The system is capable of handling various types of dental radiographs such as bitewing, panoramic, or periapical images

Introduction

Dental caries, or tooth decay, is a prevalent oral health issue caused by bacterial demineralization of enamel. Early detection is critical to prevent cavities, tooth loss, infections, and systemic complications. Traditional diagnosis relies on visual examination and radiographs, which are time-consuming and highly dependent on the clinician’s skill, potentially leading to misdiagnosis.

Advances in artificial intelligence, particularly deep learning with Convolutional Neural Networks (CNNs), allow automated detection of caries from high-resolution scanned tooth images. Scanned images capture subtle enamel changes under controlled lighting, enabling early-stage diagnosis. The proposed system integrates image preprocessing, noise reduction, data augmentation, and an optimized CNN architecture, often enhanced with transfer learning, to improve accuracy and reduce training time.

Methodology:

• Data Acquisition: High-resolution scanned images labeled by dental experts.

• Preprocessing: Images are resized, normalized, noise-filtered, and contrast-enhanced to standardize inputs for CNNs.

• Feature Extraction: CNNs automatically learn hierarchical features such as tooth edges, enamel boundaries, and texture differences.

• Data Augmentation: Techniques like flips, rotations, and color jitter increase model robustness and generalization.

• Classification: A fully connected layer outputs binary or multi-class predictions (e.g., No Caries, Early Caries, Advanced Caries). Grad-CAM visualizations highlight areas influencing the model’s decisions.

• Evaluation: Metrics include accuracy, precision, recall, F1-score, and binary cross-entropy loss.

Results:
The model successfully classified images into caries stages, demonstrating sensitivity to subtle enamel changes. Predictions include confidence scores, supporting early diagnosis and preventive intervention.

Conclusion

In conclusion, this project successfully developed an automated dental caries detection system using deep learning techniques, specifically a Convolutional Neural Network (CNN) model. Through a structured workflow that included dataset collection, preprocessing, model training, evaluation, and real-time prediction, the system achieved high accuracy and reliability in detecting and classifying caries at different stages. By integrating data augmentation, optimized CNN architecture, and performance evaluation metrics such as precision, recall, F1-score, and confusion matrix, the model demonstrated excellent generalization and clinical applicability.

References

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Copyright

Copyright © 2025 S. Swathi, M. Yazhini, V. Karpagam. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.