Detection of gastritis by a deep convolutional neural network from double-contrast upper gastrointestinal barium X-ray radiography

Togo, Ren; Yamamichi, Nobutake; Mabe, Katsuhiro et al.

Journal of Gastroenterology, 2019, 54(4), 321-329

Permalink : https://hdl.handle.net/2115/77210

このアイテムのアクセス数:1,087件（2026-08-18 04:38 集計）

閲覧可能ファイル 

ファイル フォーマット サイズ 閲覧回数 説明
jg_togo_accepte pdf 1.29 MB 1,210

論文情報

ファイル出力 EndNote Basic出力 Mendeley出力

アクセス権
open access
URI
https://hdl.handle.net/2115/77210
タイトル
Detection of gastritis by a deep convolutional neural network from double-contrast upper gastrointestinal barium X-ray radiography
その他のタイトル
Gastritis detection by deep learning
著者
Togo, Ren ; Yamamichi, Nobutake ; Mabe, Katsuhiro ; Takahashi, Yu ; Takeuchi, Chihiro ; Kato, Mototsugu ; Sakamoto, Naoya ; Ishihara, Kenta ; Ogawa, Takahiro ; Haseyama, Miki
著者名
Togo, Ren
著者名
Yamamichi, Nobutake
著者名
Mabe, Katsuhiro
著者名
Takahashi, Yu
著者名
Takeuchi, Chihiro
著者名
Kato, Mototsugu
著者名
Sakamoto, Naoya
著者名
Ishihara, Kenta
e-Rad_Researcher
20524028
著者名
Ogawa, Takahiro
e-Rad_Researcher
00218463
著者名
Haseyama, Miki
言語
英語
キーワード
Deep convolutional neural network
Artificial intelligence
Gastritis
Double-contrast upper gastrointestinal barium X-ray radiography
発行日
2019-04
出版者
Springer
収録物名
Journal of Gastroenterology
巻(号)
54(4)
ページ
321-329
抄録
Background Deep learning has become a new trend of image recognition tasks in the field of medicine. We developed an automated gastritis detection system using double-contrast upper gastrointestinal barium X-ray radiography. Methods A total of 6520 gastric X-ray images obtained from 815 subjects were analyzed. We designed a deep convolutional neural network (DCNN)-based gastritis detection scheme and evaluated the effectiveness of our method. The detection performance of our method was compared with that of ABC (D) stratification. Results Sensitivity, specificity, and harmonic mean of sensitivity and specificity of our method were 0.962, 0.983, and 0.972, respectively, and those of ABC (D) stratification were 0.925, 0.998, and 0.960, respectively. Although there were 18 false negative cases in ABC (D) stratification, 14 of those 18 cases were correctly classified into the positive group by our method.
資源タイプ
学術雑誌論文
出版タイプ
Accepted Manuscript
権利情報
The final publication is available at Springer via https://doi.org/10.1007/s00535-018-1514-7
PISSN
0944-1174
NCID
AA10988015
関連情報 (isVersionOf)
出版社版DOI
https://doi.org/10.1007/s00535-018-1514-7