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Chronic atrophic gastritis detection with a convolutional neural network considering stomach regions
This item is licensed under:Creative Commons Attribution-NonCommercial 4.0 International
| Title: | Chronic atrophic gastritis detection with a convolutional neural network considering stomach regions |
| Authors: | Kanai, Misaki Browse this author | | Togo, Ren Browse this author | | Ogawa, Takahiro Browse this author →KAKEN DB | | Haseyama, Miki Browse this author →KAKEN DB |
| Keywords: | Gastric cancer risk | | Chronic atrophic gastritis | | Helicobacter pylori | | Gastric X-ray images | | Deep learning | | Convolutional neural network | | Computer-aided diagnosis |
| Issue Date: | 7-Jul-2020 |
| Publisher: | Baishideng Publishing Group |
| Journal Title: | World Journal of Gastroenterology (The WJG Press) |
| Volume: | 26 |
| Issue: | 25 |
| Start Page: | 3650 |
| End Page: | 3659 |
| Publisher DOI: | 10.3748/wjg.v26.i25.3650 |
| Abstract: | BACKGROUND The risk of gastric cancer increases in patients withHelicobacter pylori-associated chronic atrophic gastritis (CAG). X-ray examination can evaluate the condition of the stomach, and it can be used for gastric cancer mass screening. However, skilled doctors for interpretation of X-ray examination are decreasing due to the diverse of inspections. AIM To evaluate the effectiveness of stomach regions that are automatically estimated by a deep learning-based model for CAG detection. METHODS We used 815 gastric X-ray images (GXIs) obtained from 815 subjects. The ground truth of this study was the diagnostic results in X-ray and endoscopic examinations. For a part of GXIs for training, the stomach regions are manually annotated. A model for automatic estimation of the stomach regions is trained with the GXIs. For the rest of them, the stomach regions are automatically estimated. Finally, a model for automatic CAG detection is trained with all GXIs for training. RESULTS In the case that the stomach regions were manually annotated for only 10 GXIs and 30 GXIs, the harmonic mean of sensitivity and specificity of CAG detection were 0.955 +/- 0.002 and 0.963 +/- 0.004, respectively. CONCLUSION By estimating stomach regions automatically, our method contributes to the reduction of the workload of manual annotation and the accurate detection of the CAG. |
| Rights: | http://creativecommons.org/licenses/by-nc/4.0/ |
| Type: | article |
| URI: | http://hdl.handle.net/2115/79183 |
| Appears in Collections: | 数理・データサイエンス教育研究センター (Education and Research Center for Mathematical and Data Science) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)
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