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Alfarouq, Ahmad Dzaki
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Computer Science & IT Control & Systems Engineering Education Engineering

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Web-Based Dental Caries Detection Using a Convolutional Neural Network and OpenCV Alfarouq, Ahmad Dzaki; Hadi, Ahmadul; Novaliendry, Dony; Budayawan, Khairi
Jurnal Teknologi Informasi dan Pendidikan Vol. 19 No. 1 (2026): Jurnal Teknologi Informasi dan Pendidikan (In Press)
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/jtip.v19i1.1059

Abstract

Early detection of dental caries presents a significant challenge, particularly in regions with limited access to healthcare services. While many AI models focus on binary classification, real-world applications must handle irrelevant inputs to be robust. This study develops and evaluates a web-based system using a Convolutional Neural Network (CNN) for a three-class dental image classification task: 'Caries', 'No Caries', and 'Not a Tooth'. The method employs transfer learning with the MobileNetV3 Small architecture, trained on a custom augmented dataset of 5,811 images. The model was implemented into an accessible web application using the Flask framework and OpenCV library, supporting both image upload and real-time detection. On the test set, the model achieved an overall accuracy of 93%. It demonstrated exceptional performance in rejecting irrelevant images and high reliability in identifying caries. This study presents a practical and robust tool for initial dental screening, highlighting the importance of a dedicated 'non-target' class for building trustworthy real-world AI applications in tele-dentistry.
Co-Authors Budayawan, Khairi Dony Novaliendry Hadi, Ahmadul