Abstract
This paper proposes an approach to analyse panoramic radiographs in order to automate diagnosis of impacted teeth. The panoramic radiographs go through an intensive labelling process which demarcates impacted teeth using rectangular bounding boxes. A convolutional neural network is trained on these labelled images to predict different types of impacted teeth. The empirical results illustrate good performance with respect to impacted teeth prediction.
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Notes
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The dataset is publicly available and is aimed at furthering research for object detection (https://cocodataset.org/).
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Faure, J., Engelbrecht, A. (2021). Impacted Tooth Detection in Panoramic Radiographs. In: Rojas, I., Joya, G., Català, A. (eds) Advances in Computational Intelligence. IWANN 2021. Lecture Notes in Computer Science(), vol 12861. Springer, Cham. https://doi.org/10.1007/978-3-030-85030-2_43
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DOI: https://doi.org/10.1007/978-3-030-85030-2_43
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