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Chronic atrophic gastritis detection with a convolutional neural network considering stomach regions

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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 > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

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