|
Hokkaido University Collection of Scholarly and Academic Papers >
Education and Research Center for Mathematical and Data Science >
Peer-reviewed Journal Articles, etc >
Deep convolutional neural network-based anomaly detection for organ classification in gastric X-ray examination
This item is licensed under:Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
| Title: | Deep convolutional neural network-based anomaly detection for organ classification in gastric X-ray examination |
| Authors: | Togo, Ren Browse this author | | Watanabe, Haruna Browse this author | | Ogawa, Takahiro Browse this author →KAKEN DB | | Haseyama, Miki Browse this author →KAKEN DB |
| Keywords: | Deep learning | | Medical image analysis | | Gastric X-ray examination | | Esophagus | | Stomach | | Anomaly detection | | Autoencoder |
| Issue Date: | Aug-2020 |
| Publisher: | Elsevier |
| Journal Title: | Computers in biology and medicine |
| Volume: | 123 |
| Start Page: | 103903 |
| Publisher DOI: | 10.1016/j.compbiomed.2020.103903 |
| Abstract: | Aim: The aim of this study was to determine whether our deep convolutional neural network-based anomaly detection model can distinguish differences in esophagus images and stomach images obtained from gastric X-ray examinations. Methods: A total of 6012 subjects were analyzed as our study subjects. Since the number of esophagus X-ray images is much smaller than the number of gastric X-ray images taken in X-ray examinations, we took an anomaly detection approach to realize the task of organ classification. We constructed a deep autoencoding gaussian mixture model (DAGMM) with a convolutional autoencoder architecture. The trained model can produce an anomaly score for a given test X-ray image. For comparison, the original DAGMM, AnoGAN, and a One-Class Support Vector Machine (OCSVM) that were trained with features obtained by a pre-trained Inception-v3 network were used. Results: Sensitivity, specificity, and the calculated harmonic mean of the proposed method were 0.956, 0.980, and 0.968, respectively. Those of the original DAGMM were 0.932, 0.883, and 0.907, respectively. Those of AnoGAN were 0.835, 0.833, and 0.834, respectively, and those of OCSVM were 0.932, 0.935, and 0.934, respectively. Experimental results showed the effectiveness of the proposed method for an organ classification task. Conclusion: Our deep convolutional neural network-based anomaly detection model has shown the potential for clinical use in organ classification. |
| Rights: | © 2020. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ | | http://creativecommons.org/licenses/by-nc-nd/4.0/ |
| Type: | article (author version) |
| URI: | http://hdl.handle.net/2115/82588 |
| 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)
|
Submitter: 藤後 廉
|
