T
Takuma Funakoshi
Researcher at Aichi Gakuin University
Publications - 8
Citations - 194
Takuma Funakoshi is an academic researcher from Aichi Gakuin University. The author has contributed to research in topics: Medicine & Deep learning. The author has an hindex of 3, co-authored 7 publications receiving 76 citations.
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Journal ArticleDOI
Deep-learning classification using convolutional neural network for evaluation of maxillary sinusitis on panoramic radiography
Makoto Murata,Yoshiko Ariji,Yasufumi Ohashi,Taisuke Kawai,Motoki Fukuda,Takuma Funakoshi,Yoshitaka Kise,Michihito Nozawa,Akitoshi Katsumata,Hiroshi Fujita,Eiichiro Ariji +10 more
TL;DR: The diagnostic performance of the deep-learning system for maxillary sinusitis on panoramic radiographs was sufficiently high and is expected to provide diagnostic support for inexperienced dentists.
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Comparison of 3 deep learning neural networks for classifying the relationship between the mandibular third molar and the mandibular canal on panoramic radiographs
Motoki Fukuda,Yoshiko Ariji,Yoshitaka Kise,Michihito Nozawa,Chiaki Kuwada,Takuma Funakoshi,Chisako Muramatsu,Hiroshi Fujita,Akitoshi Katsumata,Eiichiro Ariji +9 more
TL;DR: The size of the image patches should be carefully determined to ensure acquisition of high diagnostic performance and consistency among 3 image recognition convolutional neural networks in the evaluation of the relationships between the mandibular third molar and theMandibular canal on panoramic radiographs.
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Usefulness of a deep learning system for diagnosing Sjögren’s syndrome using ultrasonography images
Yoshitaka Kise,Mayumi Shimizu,Haruka Ikeda,Takeshi Fujii,Chiaki Kuwada,Masako Nishiyama,Takuma Funakoshi,Yoshiko Ariji,Hiroshi Fujita,Akitoshi Katsumata,Kazunori Yoshiura,Eiichiro Ariji +11 more
TL;DR: The performance of the deep learning system for diagnosing SjS from the US images was compared with the diagnoses made by three inexperienced radiologists, suggesting that deep learning could be used for diagnostic support when interpreting US images.
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Performance of deep learning technology for evaluation of positioning quality in periapical radiography of the maxillary canine.
Mizuho Mori,Yoshiko Ariji,Motoki Fukuda,Tomoya Kitano,Takuma Funakoshi,Wataru Nishiyama,Kiyomi Kohinata,Yukihiro Iida,Eiichiro Ariji,Akitoshi Katsumata +9 more
TL;DR: In this paper, the authors created and tested two deep learning systems using 500 periapical radiographs (250 each of good and bad-quality images) and assigned 350, 70, and 80 images as the training, validation, and test datasets, respectively.
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Detection and classification of unilateral cleft alveolus with and without cleft palate on panoramic radiographs using a deep learning system.
Chiaki Kuwada,Yoshiko Ariji,Yoshitaka Kise,Takuma Funakoshi,Motoki Fukuda,Tsutomu Kuwada,Kenichi Gotoh,Eiichiro Ariji +7 more
TL;DR: A computer-aided diagnosis system for diagnosing the CA status on panoramic radiographs using a deep learning object detection technique with and without normal data in the learning process is developed, to verify its performance, and to clarify some characteristic appearances probably related to the performance.