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Architecture of the deep learning image quality evaluation model. The extracted 32 slices along the 3 plane (axial, coronal, and sagittal) were used as input to DCNN to predict the quality of each slice. The slice quality scores were next used as input to a fully-connected (FC) network to predict the volume-wise quality. An ensemble model was constructed by averaging the image quality scores from the three cascaded networks.
