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Automatic human identification from panoramic dental radiographs using the convolutional neural network
شناسایی خودکار انسان از رادیوگرافی دندانپزشکی پانوراما با استفاده از شبکه عصبی کانولوشن-2020 Human identification is an important task in mass disaster and criminal investigations. Although several
automatic dental identification systems have been proposed, accurate and fast identification from
panoramic dental radiographs (PDRs) remains a challenging issue. In this study, an automatic human
identification system (DENT-net) was developed using the customized convolutional neural network
(CNN). The DENT-net was trained on 15,369 PDRs from 6300 individuals. The PDRs were preprocessed by
affine transformation and histogram equalization. The DENT-net took 128 128 7 square patches as
input, including the whole PDR and six details extracted from the PDR. Using the DENT-net, the feature
extraction took around 10 milliseconds per image and the running time for retrieval was 33.03
milliseconds in a 2000-individual database, promising an application on larger databases. The
visualization of CNN showed that the teeth, maxilla, and mandible all contributed to human
identification. The DENT-net achieved Rank-1 accuracy of 85.16% and Rank-5 accuracy of 97.74% for
human identification. The present results demonstrated that human identification can be achieved from
PDRs by CNN with high accuracy and speed. The present system can be used without any special
equipment or knowledge to generate the candidate images. While the final decision should be made by
human specialists in practice. It is expected to aid human identification in mass disaster and criminal
investigation Keywords: Forensic odontology | Human identification | Panoramic dental radiographs | Deep learning | Convolutional neural network |
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