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KMID : 0362120200420010001
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Journal of Technologic Dentistry
2020 Volume.42 No. 1 p.1 ~ p.8
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A Study on Virtual Tooth Image Generation Using Deep Learning ? Based on the number of learning
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Bae Eun-Jeong
Jeong Jun-Ho
Son Yun-Sik
Lim Joon-Yeon
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Abstract
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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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KEYWORD
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Deep Convolutional Generative Adversarial Networks, Deep learning, Lower first molar, Number of learning, Virtual tooth
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