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    A hybrid mask RCNN-based tool to localize dental cavities from real-time mixed photographic images

    Rashid, Umer, Javid, Aiman, Khan, Abdur Rehman, Liu, Leo, Ahmed, Adeel, Khalid, Osman, Saleem, Khalid, Meraj, Shaista, Iqbal, Uzair and Nawaz, Raheel ORCID logoORCID: https://orcid.org/0000-0001-9588-0052 (2022) A hybrid mask RCNN-based tool to localize dental cavities from real-time mixed photographic images. PeerJ Computer Science, 8. e888-e888.

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    Abstract

    Nearly 3.5 billion humans have oral health issues, including dental caries, which requires dentist-patient exposure in oral examinations. The automated approaches identify and locate carious regions from dental images by localizing and processing either colored photographs or X-ray images taken <jats:italic>via</jats:italic> specialized dental photography cameras. The dentists’ interpretation of carious regions is difficult since the detected regions are masked using solid coloring and limited to a particular dental image type. The software-based automated tools to localize caries from dental images taken <jats:italic>via</jats:italic> ordinary cameras requires further investigation. This research provided a mixed dataset of dental photographic (colored or X-ray) images, instantiated a deep learning approach to enhance the existing dental image carious regions’ localization procedure, and implemented a full-fledged tool to present carious regions <jats:italic>via</jats:italic> simple dental images automatically. The instantiation mainly exploits the mixed dataset of dental images (colored photographs or X-rays) collected from multiple sources and pre-trained hybrid Mask RCNN to localize dental carious regions. The evaluations performed by the dentists showed that the correctness of annotated datasets is up to 96%, and the accuracy of the proposed system is between 78% and 92%. Moreover, the system achieved the overall satisfaction level of dentists above 80%.

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