Automatic detection and classification of radiolucent lesions in the mandible on panoramic radiographs using a deep learning object detection technique.

Abstract

Authors

• Yoshiko Ariji
  Department of Oral Radiology, School of Dentistry, Osaka Dental University, Osaka, Japan.
• Yudai Yanashita
  Postgraduate student, Department of Electrical, Electronic and Computer Faculty of Engineering, Gifu University, Gifu, Japan.
• Syota Kutsuna
  Postgraduate student, Department of Electrical, Electronic and Computer Faculty of Engineering, Gifu University, Gifu, Japan.
• Chisako Muramatsu
  Department of Intelligent Image Information, Graduate School of Medicine, Gifu University, 1-1 Yanagido, Gifu, Gifu 501-1194, Japan. Electronic address: chisa@fjt.info.gifu-u.ac.jp.
• Motoki Fukuda
  Department of Oral Radiology, School of Dentistry, Osaka Dental University, Osaka, Japan.
• Yoshitaka Kise
  Department of Oral and Maxillofacial Radiology, Aichi Gakuin University School of Dentistry, Nagoya, Japan.
• Michihito Nozawa
  Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of Dentistry, 2-11 Suemori-dori, Chikusa-ku, Nagoya, 464-8651, Japan.
• Chiaki Kuwada
  Department of Oral and Maxillofacial Radiology, Aichi Gakuin University School of Dentistry, Nagoya, Japan.
• Hiroshi Fujita
  Department of Intelligent Image Information, Division of Regeneration and Advanced Medical Sciences, Graduate School of Medicine, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan.
• Akitoshi Katsumata
• Eiichiro Ariji
  Department of Oral and Maxillofacial Radiology, Aichi Gakuin University School of Dentistry, Nagoya, Japan.

Keywords

External Resources