Caries level classification based on Zernike moment invariant and machine learning
Author:
Publisher
AIP Publishing
Link
http://aip.scitation.org/doi/pdf/10.1063/5.0182253
Reference12 articles.
1. Suratri, M.A.L., Jovina, T.A., and Notohartojo, I.: ‘Pengaruh (pH) saliva terhadap terjadinya karies gigi pada anak usia prasekolah’, Buletin Penelitian Kesehatan, 2017, 45, (4), pp. 241–248
2. Edge Detection and Level Set Active Contour Model for the Segmentation of Cavity Present in Dental X-Ray Images
3. Obuchowicz, R., Nurzynska, K., Obuchowicz, B., Urbanik, A., and Piórkowski, A.: ‘Caries detection enhancement using texture feature maps of intraoral radiographs’, in Editor (Ed.)^(Eds.): ‘Book Caries detection enhancement using texture feature maps of intraoral radiographs’ (Springer, 2018, edn.), pp.
4. Dental diagnosis from X-Ray images: An expert system based on fuzzy computing
5. Jusman, Y., Tamarena, R.I., Puspita, S., Saleh, E., and Kanafiah, S.N.A.M.: ‘Analysis of Features Extraction Performance to Differentiate of Dental Caries Types Using Gray Level Co-occurrence Matrix Algorithm’, in Editor (Ed.)^(Eds.): ‘Book Analysis of Features Extraction Performance to Differentiate of Dental Caries Types Using Gray Level Co-occurrence Matrix Algorithm’ (IEEE, 2020, edn.), pp. 148–152
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