Journal of Technologic Dentistry (대한치과기공학회지)

Korean Academy of Dental Technology (대한치과기공학회)
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A Study on Virtual Tooth Image Generation Using Deep Learning - Based on the number of learning

심층 학습을 활용한 가상 치아 이미지 생성 연구 -학습 횟수를 중심으로

  • Bae, EunJeong (Dept. of Mechanical Robotics and Energy Engineering, Dongguk University) ;
  • Jeong, Junho (Dept. of Computer Science & Engineering, Kongju University) ;
  • Son, Yunsik (Dept. of Computer Science & Engineering, Dongguk University) ;
  • Lim, JoonYeon (Dept. of Mechanical Robotics and Energy Engineering, Dongguk University)
  • 배은정 (동국대학교 기계로봇에너지공학과) ;
  • 정준호 (공주대학교 컴퓨터공학과) ;
  • 손윤식 (동국대학교 컴퓨터공학과) ;
  • 임중연 (동국대학교 기계로봇에너지공학과)
  • Received : 2019.11.25
  • Accepted : 2020.03.03
  • Published : 2020.04.01
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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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