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A Study on Virtual Tooth Image Generation Using Deep Learning - Based on the number of learning
3
Zitationen
4
Autoren
2020
Jahr
Abstract
Purpose: Among the virtual teeth generated by Deep Convolutional Generative Adversarial Networks (DCGAN), the optimal data was analyzed for the number of learning.Methods: We extracted 50 mandibular first molar occlusal surfaces and trained 4,000 epoch with DCGAN.The learning screen was saved every 50 times and evaluated on a Likert 5-point scale according to five classification criteria.Results were analyzed by one-way ANOVA and tukey HSD post hoc analysis (α = 0.05).Results: It was the highest with 83.90±6.32 in the number of group3 (2,050-3,000) learning and statistically significant in the group1 (50-1,000) and the group2 (1,050-2,000). Conclusion:Since there is a difference in the optimal virtual tooth generation according to the number of learning, it is necessary to analyze the learning frequency section in various ways.
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