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

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Please use this identifier to cite or link to this item:http://hdl.handle.net/2115/77210

Title: Detection of gastritis by a deep convolutional neural network from double-contrast upper gastrointestinal barium X-ray radiography
Other Titles: Gastritis detection by deep learning
Authors: Togo, Ren Browse this author
Yamamichi, Nobutake Browse this author
Mabe, Katsuhiro Browse this author
Takahashi, Yu Browse this author
Takeuchi, Chihiro Browse this author
Kato, Mototsugu Browse this author
Sakamoto, Naoya Browse this author
Ishihara, Kenta Browse this author
Ogawa, Takahiro Browse this author →KAKEN DB
Haseyama, Miki Browse this author →KAKEN DB
Keywords: Deep convolutional neural network
Artificial intelligence
Gastritis
Double-contrast upper gastrointestinal barium X-ray radiography
Issue Date: Apr-2019
Publisher: Springer
Journal Title: Journal of Gastroenterology
Volume: 54
Issue: 4
Start Page: 321
End Page: 329
Publisher DOI: 10.1007/s00535-018-1514-7
Abstract: 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.
Rights: The final publication is available at Springer via https://doi.org/10.1007/s00535-018-1514-7
Type: article (author version)
URI: http://hdl.handle.net/2115/77210
Appears in Collections:情報科学院・情報科学研究院 (Graduate School of Information Science and Technology / Faculty of Information Science and Technology) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

Submitter: 藤後 廉

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