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The study addresses dental caries detection and classification using state-of-the-art deep learning architectures. We implemented and compared three pre-trained convolutional neural network models: VGG19, DenseNet169, and ResNet101, to automatically identify and classify dental caries from intraoral clinical image. Our research focused specifically on pediatric populations aged 1 to 14 years, where caries remain a significant health concern despite global prevention efforts. The models were trained and validated on a comprehensive dataset of dental images. Performance metrics demonstrated that DenseNet169 model achieved superior results with an Validation accuracy of 72.22%. These deep learning approaches show promising potential to augment traditional diagnostic methods, particularly in resource-limited settings where expert dental practitioners may be scarce. By enabling earlier and more accurate detection of carious lesions, our proposed system could help address disparities in oral healthcare accessibility and contribute to more effective intervention strategies, especially for underprivileged populations where caries prevalence continues to rise. This research establishes a technological framework that could be integrated into portable diagnostic tools for use in diverse clinical environments.

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Pediatric Dental Caries Classification Using Deep Learning: An Empirical Comparison of CNN Architectures

  • 会议论文

  • 发表日期：

    2026年

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  • Pranav Bagal,
  • Bhavesh Patil,
  • Shounak Muglikar,
  • Yash Sonavane,
  • Mrs. Prajakta S. Shinde

摘要

The study addresses dental caries detection and classification using state-of-the-art deep learning architectures. We implemented and compared three pre-trained convolutional neural network models: VGG19, DenseNet169, and ResNet101, to automatically identify and classify dental caries from intraoral clinical image. Our research focused specifically on pediatric populations aged 1 to 14 years, where caries remain a significant health concern despite global prevention efforts. The models were trained and validated on a comprehensive dataset of dental images. Performance metrics demonstrated that DenseNet169 model achieved superior results with an Validation accuracy of 72.22%. These deep learning approaches show promising potential to augment traditional diagnostic methods, particularly in resource-limited settings where expert dental practitioners may be scarce. By enabling earlier and more accurate detection of carious lesions, our proposed system could help address disparities in oral healthcare accessibility and contribute to more effective intervention strategies, especially for underprivileged populations where caries prevalence continues to rise. This research establishes a technological framework that could be integrated into portable diagnostic tools for use in diverse clinical environments.