Abstract
Oral health has become increasingly important because it helps gain confidence in communication and work. Therefore, detecting and diagnosing teeth as early as possible is essential to reduce adverse patient effects and protect oral health. Nowadays the importance of the oral cavity is recognized as a vital part of the human body. However, it affects health status and causes mental and work productivity problems. This study proposes a model applying the Unet3+ model to classify and detect teeth in the jaw. The dataset consists of anonymized and deidentified panoramic dental X-rays of 116 patients at Noor Medical Imaging Center, Qom, Iran. The subjects cover various dental conditions, from healthy to partial and complete edentulous cases. The experimental results show Unet3+ model achieves promising results, with Accuracy being 94.80 and F1-score being 0.9533.
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Luong, H.H. et al. (2023). Utilising Unet3+ for Tooth Segmentation on X-Ray Image. In: Nguyen, N.T., et al. Recent Challenges in Intelligent Information and Database Systems. ACIIDS 2023. Communications in Computer and Information Science, vol 1863. Springer, Cham. https://doi.org/10.1007/978-3-031-42430-4_15
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DOI: https://doi.org/10.1007/978-3-031-42430-4_15
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