Abstract
Detection of treatment types in dental panoramic radiographs is still an open problem as the position of the tooth are arbitrarily oriented and usually closely packed. The majority of current two-stage anchor-based detectors are used in oriented object detection techniques. Nevertheless, the positive and negative anchor boxes tend to be severely biased in anchor-based detectors. In this work, we optimized a single-stage anchor-free deep learning model to detect and classify the teeth with or without treatment. We aim to detect dental restoration, root canal treatment (RCT), and teeth without treatment accurately in a full scan of dental panoramic radiographs. We trained our model on 500 images and tested it on 93 images from a dataset of 593 dental panoramic x-rays. The proposed work performance on overall dental treatment detection with an average precision (AP) of 85%. The result of this study suggested that RCT was recognized and predicted with the highest accuracy of 91% AP score.
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GitHub repository link with the entire python code: https://github.com/Nripendrakr123/Detection_of_tooth_treatment_type.
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Singh, N.K., Faisal, M., Hasan, S., Goshwami, G., Raza, K. (2023). Dental Treatment Type Detection in Panoramic X-Rays Using Deep Learning. In: Abraham, A., Pllana, S., Casalino, G., Ma, K., Bajaj, A. (eds) Intelligent Systems Design and Applications. ISDA 2022. Lecture Notes in Networks and Systems, vol 716. Springer, Cham. https://doi.org/10.1007/978-3-031-35501-1_3
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