Abstract
Dental caries is an immensely frequent complication in dental sector which creates a greater impact on the majority portion of the population. Dental caries is very herculean to detect as their location makes clinical analysis strenuous. Compared to traditional methods, the modern approaches are faster, less labor-intensive, and more precise. Advanced computation algorithms and transfer learning models are increasingly utilized in dentistry to improve the efficiency of detecting and classifying periapical lesions and caries. The data augmentation and automatic feature extraction process for training and testing classification models include multiple iterations. Nonetheless, inaccurate results may hinder the diagnostic procedures. To help dentists enhance the efficiency of caries detection, cutting-edge computing techniques along with pre-trained architecture like VGG-16 are utilized. In this paper, the optimal outcomes were accomplished by VGG-16 algorithm with steady learning rate of 0.001 and Adam optimizer displaying accuracy on the validation data as 98.70. The execution provides a wider objective and application of the model in dentistry.
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Rajput, D., Rane, H., Nikam, D., Wagh, J., Jadhav, A. (2023). Detection and Classification of Dental Caries Using Deep and Transfer Learning. In: Das, A.K., Nayak, J., Naik, B., Vimal, S., Pelusi, D. (eds) Computational Intelligence in Pattern Recognition. CIPR 2022. Lecture Notes in Networks and Systems, vol 725. Springer, Singapore. https://doi.org/10.1007/978-981-99-3734-9_3
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DOI: https://doi.org/10.1007/978-981-99-3734-9_3
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