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The prediction in computer color matching of dentistry based on GA+BP neural network.

Li H, Lai L, Chen L, Lu C, Cai Q - Comput Math Methods Med (2015)

Bottom Line: Although the use of computer color matching can reduce the influence of subjective factors by technicians, matching the color of a natural tooth with a ceramic restoration is still one of the most challenging topics in esthetic prosthodontics.To our knowledge, we firstly combine the BPNN with GA in computer color matching in dentistry.Extensive experiments demonstrate that the proposed method improves the precision and prediction robustness of the color matching in restorative dentistry.

View Article: PubMed Central - PubMed

Affiliation: School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China.

ABSTRACT
Although the use of computer color matching can reduce the influence of subjective factors by technicians, matching the color of a natural tooth with a ceramic restoration is still one of the most challenging topics in esthetic prosthodontics. Back propagation neural network (BPNN) has already been introduced into the computer color matching in dentistry, but it has disadvantages such as unstable and low accuracy. In our study, we adopt genetic algorithm (GA) to optimize the initial weights and threshold values in BPNN for improving the matching precision. To our knowledge, we firstly combine the BPNN with GA in computer color matching in dentistry. Extensive experiments demonstrate that the proposed method improves the precision and prediction robustness of the color matching in restorative dentistry.

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Comparisons of predictive ability of BPNN with different number of hidden layer nodes.
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fig5: Comparisons of predictive ability of BPNN with different number of hidden layer nodes.

Mentions: In order to get the specific number of hidden layer nodes, we introduced the ideas of trial and error and conducted a series of 10 trials. Each trial of test performed 20 times of prediction. The experimental data is training data set referred to in the previous section. Different trials have different hidden layer nodes while other parameters in different trials are consistent. The experiment results are shown in Figure 5.


The prediction in computer color matching of dentistry based on GA+BP neural network.

Li H, Lai L, Chen L, Lu C, Cai Q - Comput Math Methods Med (2015)

Comparisons of predictive ability of BPNN with different number of hidden layer nodes.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4385598&req=5

fig5: Comparisons of predictive ability of BPNN with different number of hidden layer nodes.
Mentions: In order to get the specific number of hidden layer nodes, we introduced the ideas of trial and error and conducted a series of 10 trials. Each trial of test performed 20 times of prediction. The experimental data is training data set referred to in the previous section. Different trials have different hidden layer nodes while other parameters in different trials are consistent. The experiment results are shown in Figure 5.

Bottom Line: Although the use of computer color matching can reduce the influence of subjective factors by technicians, matching the color of a natural tooth with a ceramic restoration is still one of the most challenging topics in esthetic prosthodontics.To our knowledge, we firstly combine the BPNN with GA in computer color matching in dentistry.Extensive experiments demonstrate that the proposed method improves the precision and prediction robustness of the color matching in restorative dentistry.

View Article: PubMed Central - PubMed

Affiliation: School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China.

ABSTRACT
Although the use of computer color matching can reduce the influence of subjective factors by technicians, matching the color of a natural tooth with a ceramic restoration is still one of the most challenging topics in esthetic prosthodontics. Back propagation neural network (BPNN) has already been introduced into the computer color matching in dentistry, but it has disadvantages such as unstable and low accuracy. In our study, we adopt genetic algorithm (GA) to optimize the initial weights and threshold values in BPNN for improving the matching precision. To our knowledge, we firstly combine the BPNN with GA in computer color matching in dentistry. Extensive experiments demonstrate that the proposed method improves the precision and prediction robustness of the color matching in restorative dentistry.

Show MeSH
Related in: MedlinePlus