Automating chest radiograph imaging quality control
نویسندگان
چکیده
PurposeTo automate diagnostic chest radiograph imaging quality control (lung inclusion at all four edges, patient rotation, and correct inspiration) using convolutional neural network models.MethodsThe data comprised of 2589 postero-anterior radiographs imaged in a standing position, which were divided into train, validation, test sets. We increased the number images for by cropping appropriate images, rotation flipping horizontally. The image histograms equalized, resized to 512 × resolution. trained six networks models detect features manual annotations as training targets. Additionally, we studied inter-observer variability annotation.ResultsThe networks’ areas under receiver operating characteristic curve >0.88 inclusions, >0.70 >0.79 inspiration, respectively. agreement between two human annotators assessed image-quality were: 92%, 90%, 82%, 88% patient’s left, right, cranial, caudal 78% 89% Higher was related smaller variance confidence.ConclusionsThe developed provide automated tools radiological department. could be used obtain immediate feedback quality, serve an educational instrument.
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ژورنال
عنوان ژورنال: Physica Medica
سال: 2021
ISSN: ['1724-191X', '1120-1797']
DOI: https://doi.org/10.1016/j.ejmp.2021.03.014