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In this study, the application of advanced deep learning techniques for predicting various dental diseases is explored through a multiclass classification framework. The proposed model is built upon a comprehensive database containing five distinct dental conditions: tooth stains, gingivitis, mouth ulcers, cavities, and calculus. Utilizing DenseNet201 architecture, the model facilitates early detection and delivers accurate diagnostic decisions, serving as a reliable decision-support tool for dental professionals. This system aims to enhance clinical efficiency, reduce diagnostic errors, and promote timely treatment interventions. The model employs DenseNet201, which is renowned for its high performance in image classification tasks and is shown to have the ability to detect intricate patterns within dental images for accurate diagnostics. The research also highlights the fact that deep learning profoundly impacts the healthcare domain, primarily by increasing the accuracy of diagnostics and improving clinical procedures. Ultimately, this approach enables better patient care by facilitating early diagnosis, individualized treatment, and preventive measures of healthcare.

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Deep Learning-Based Multiclass Classification for Dental Disease Detection Using DenseNet-201

  • 会议论文

  • 发表日期：

    2026年

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  • Adithya B. Chandran,
  • S. Sheron,
  • Nehal Sreejith,
  • V. M. Harshini,
  • S. Murugaveni

摘要

In this study, the application of advanced deep learning techniques for predicting various dental diseases is explored through a multiclass classification framework. The proposed model is built upon a comprehensive database containing five distinct dental conditions: tooth stains, gingivitis, mouth ulcers, cavities, and calculus. Utilizing DenseNet201 architecture, the model facilitates early detection and delivers accurate diagnostic decisions, serving as a reliable decision-support tool for dental professionals. This system aims to enhance clinical efficiency, reduce diagnostic errors, and promote timely treatment interventions. The model employs DenseNet201, which is renowned for its high performance in image classification tasks and is shown to have the ability to detect intricate patterns within dental images for accurate diagnostics. The research also highlights the fact that deep learning profoundly impacts the healthcare domain, primarily by increasing the accuracy of diagnostics and improving clinical procedures. Ultimately, this approach enables better patient care by facilitating early diagnosis, individualized treatment, and preventive measures of healthcare.