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Hokkaido University Collection of Scholarly and Academic Papers >
Graduate School of Health Sciences / Faculty of Health Sciences >
Peer-reviewed Journal Articles, etc >
Quality Assurance of Chest X-ray Images with a Combination of Deep Learning Methods
| Title: | Quality Assurance of Chest X-ray Images with a Combination of Deep Learning Methods |
| Authors: | Oura, Daisuke Browse this author | | Sato, Shinpe Browse this author | | Honma, Yuto Browse this author | | Kuwajima, Shiho Browse this author | | Sugimori, Hiroyuki Browse this author →KAKEN DB |
| Keywords: | quality assurance | | X-ray | | deep learning | | artificial intelligence |
| Issue Date: | 5-Feb-2023 |
| Publisher: | MDPI |
| Journal Title: | Applied sciences |
| Volume: | 13 |
| Issue: | 4 |
| Start Page: | 2067 |
| Publisher DOI: | 10.3390/app13042067 |
| Abstract: | Background: Chest X-ray (CXR) imaging is the most common examination; however, no automatic quality assurance (QA) system using deep learning (DL) has been established for CXR. This study aimed to construct a DL-based QA system and assess its usefulness. Method: Datasets were created using over 23,000 images from Chest-14 and clinical images. The QA system consisted of three classification models and one regression model. The classification method was used for the correction of image orientation, left-right reversal, and estimating the patient's position, such as standing, sitting, and lying. The regression method was used for the correction of the image angle. ResNet-50, VGG-16, and the original convolutional neural network (CNN) were compared under five cross-fold evaluations. The overall accuracy of the QA system was tested using clinical images. The mean correction time of the QA system was measured. Result: ResNet-50 demonstrated higher performance in the classification. The original CNN was preferred in the regression. The orientation, angle, and left-right reversal of all images were fully corrected in all images. Moreover, patients' positions were estimated with 96% accuracy. The mean correction time was approximately 0.4 s. Conclusion: The DL-based QA system quickly and accurately corrected CXR images. |
| Type: | article |
| URI: | http://hdl.handle.net/2115/88945 |
| Appears in Collections: | 保健科学院・保健科学研究院 (Graduate School of Health Sciences / Faculty of Health Sciences) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)
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