Identification of enamel demineralization using high performance convolutional neural network

CARAMEL-JUVINO, AMANDAFAROOQ, SAJIDROMANO, MARIANAZEZELL, DENISE M.2023-01-262023-01-26CARAMEL-JUVINO, AMANDA; FAROOQ, SAJID; ROMANO, MARIANA; ZEZELL, DENISE M. Identification of enamel demineralization using high performance convolutional neural network. In: SBFOTON INTERNATIONAL OPTICS AND PHOTONICS CONFERENCE, October 13-15, 2022, Recife, PE. <b>Proceedings...</b> Piscataway, NJ, USA: IEEE, 2022. DOI: <a href="https://dx.doi.org/10.1109/SBFotonIOPC54450.2022.9992381">10.1109/SBFotonIOPC54450.2022.9992381</a>. Disponível em: http://repositorio.ipen.br/handle/123456789/33668.http://repositorio.ipen.br/handle/123456789/33668Here, we traces use segmentation and convolutional neural network (CNN) to trace, diagnose and quantify enamel demineralization for research. The preprocessing, histograms based methods are used to enhance the contrast and equalize the brightness through the scanning electron microscope images. Our result evidence that the deep learning based CNN model is highly efficient to process the dental image to achieve high accuracy of enamel demineralization and presents promising outcomes with optimal precision.openAccessneural networksdentistryenamelsdemineralizationlearningIdentification of enamel demineralization using high performance convolutional neural networkTexto completo de evento10.1109/SBFotonIOPC54450.2022.99923810000-0001-7404-9606https://orcid.org/0000-0001-7404-9606