심층 학습을 활용한 가상 치아 이미지 생성 연구 ?학습 횟수를 중심으로
A Study on Virtual Tooth Image Generation Using Deep Learning ? Based on the number of learning
배은정, 정준호, 손윤식, 임중연,
소속 상세정보
배은정 ( Bae Eun-Jeong ) - Dongguk University Department of Mechanical Robotics and Energy Engineering
정준호 ( Jeong Jun-Ho ) - Kongju University Department of Computer Science & Engineering
손윤식 ( Son Yun-Sik ) - Dongguk University Department of Computer Science & Engineering
임중연 ( Lim Joon-Yeon ) - Dongguk University Department of Mechanical Robotics and Energy Engineering
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.
키워드
Deep Convolutional Generative Adversarial Networks; Deep learning; Lower first molar; Number of learning; Virtual tooth
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