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https://hdl.handle.net/2115/88945

Quality Assurance of Chest X-ray Images with a Combination of Deep Learning Methods

英語

artificial intelligence

2023-02-05

MDPI

Applied sciences

13(4)

2067

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.

学術雑誌論文

Version of Record

2076-3417