Abstract
The proposed work aims at implementing a Deep Convolutional Neural Network algorithm specialized in object detection. It was trained to perform tooth detection, segmentation, classification and labelling on panoramic dental radiographs. A dataset of dental panoramic radiographs was annotated according to the FDI tooth numbering system. Mask R-CNN Inception ResNet V2 object detection algorithm was able to give excellent results in terms of tooth segmentation and numbering. The experimental results were validated using standard performance metrics. The method could not only give comparable results to that of similar works but could detect even missing teeth, unlike similar works.
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Shirsat, S., Abraham, S. (2021). Tooth Detection from Panoramic Radiographs Using Deep Learning. In: Srirama, S.N., Lin, J.CW., Bhatnagar, R., Agarwal, S., Reddy, P.K. (eds) Big Data Analytics. BDA 2021. Lecture Notes in Computer Science(), vol 13147. Springer, Cham. https://doi.org/10.1007/978-3-030-93620-4_5
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DOI: https://doi.org/10.1007/978-3-030-93620-4_5
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