| 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)
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