Incremental deep learning for reflectivity data recognition in stomatology

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60461373%3A22340%2F22%3A43924512" target="_blank" >RIV/60461373:22340/22:43924512 - isvavai.cz</a>
Alternative codes found
RIV/00216208:11110/22:10438736 RIV/00216208:11150/22:10438736 RIV/68407700:21730/22:00363950
Result on the web
<a href="https://link.springer.com/article/10.1007/s00521-021-06842-6" target="_blank" >https://link.springer.com/article/10.1007/s00521-021-06842-6</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s00521-021-06842-6" target="_blank" >10.1007/s00521-021-06842-6</a>

Result language
angličtina
Original language name
Incremental deep learning for reflectivity data recognition in stomatology
Original language description
The recognition of stomatological disorders and the classification of dental caries are important areas of biomedicine that can hugely benefit from machine learning tools for the construction of relevant mathematical models. This paper explores the possibility of using reflectivity data to distinguish between healthy tissues and caries by deep learning and multilayer convolutional neural networks. The experimental data set includes more than 700 observations recorded in the stomatology laboratory. For rigor, the results obtained from the deep learning systems are compared with those evaluated for selected sets of features estimated for each observation and classified by a decision tree, support vector machine (SVM), k-nearest neighbor, Bayesian methods, and two-layer neural networks. The classification accuracy obtained for the deep learning systems was 98.1% and 94.4% for data in the signal and spectral domains, respectively, in comparison with an accuracy of 97.2% and 87.2% evaluated by the SVM method. The proposed method conclusively demonstrates how the artificial intelligence and deep learning methodology can contribute to improved diagnosis of dental problem in stomatology. © 2021, The Author(s).
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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