<![CDATA[Image quality in radiography is essential for diagnostic accuracy, yet reject–repeat analysis (RRA) is still mostly performed manually and depends on subjective radiographer judgment. Inter-operator variability leads to inconsistent quality data and limits workflow optimization. While many studies evaluate deep learning models for disease classification, research using convolutional neural networks (CNNs) to assess image quality and automate RRA remains limited. This study introduces a novel approach by applying CNNs to classify the causes of image rejection in chest radiography: positioning, exposure, artefacts, and accepted images, integrating these outcomes into a quality-management framework. The aim is to develop and evaluate CNN models (visual geometry group (VGG)-16, residual neural network (ResNet)-101, and dense convolutional network (DenseNet)-201) to support automated RRA using digital imaging and communications in medicine (DICOM)-based radiographs. The dataset comprised DICOM chest images annotated by a panel of radiologists, radiographers, and medical physicists into four technical classes. The CNNs were trained using supervised learning with structured training, validation, and testing splits, and evaluated using accuracy, precision, recall, and F1-score. Results show VGG-16 delivers the most stable and accurate performance across categories. Overall, CNNs effectively automate RRA with greater objectivity and consistency, reducing repeat rates, improving workflow efficiency, and strengthening radiology quality-management systems to enhance patient safety.]]>
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