Introduction: Nowadays, general radiography still represents primary imaging technique, therefore the need to improve the quality of x-ray images is more than justified. Despite the great technological advancement of digital radiography, we still detect artefacts, which lead to poor quality radiograms. Purpose: Purpose of study was to develop automated detection of artefacts on CR images with convolutional neural network (CNN). Methods: According to a predetermined protocol, we obtained 269 homogeneous images with 31 CR plates. Colection was evaluated by two experts who determined the presence of artifacts. Artifacts were classified into five groups - dust, dirt and cracks, ghosting and non-uniformity, straight lines, serrated artefacts and others. The images were preprocessed and entered into CNN AlexNet, which was custumized with transfer learning process. In order to learn the recognition of each group of artifacts, we divided colection of images into learning (80 %) and testing (20 %) part. The CNN gives the probability of the presence of an artefact for each image. Validation of learned neural network for each group of artifacts was the final step, where the AUC measure was chosen as the main measure of effectiveness. Results: The best results were achieved in groups of serrated artefacts (AUC= 100 %), others (AUC= 99.02 %) and ghosting and non-uniformity (AUC=97.62 %). In the group of serrated artifacts and others we captured only a few representatives of these artefacts, however, we can conclude that both groups were successfully detected, probably because of the easy noticeable look of artefact. Slightly lover detection of artefacts can be observed in groups of local artifacts - dust, dirt and crack (AUC= 83.70 %), and lines (AUC= 81.21 %), since the preprocessing of an image for input into the CNN AlexNet requires scaling the image dimensions to 227×227 pixels. Artifacts of smaller dimensions are therefore lost by scaling the image. Discussion and conclusion: Deep learning method by teaching and adapting the CNN AlexNet has proven to be very efficient model for detection of artefacts in CR images. From the results of the study we can conclude that the automatic detection of artefacts with the CNN is most effective in detecting global artefacts. However we can confirm high ability of system to detect the most frequently present artefacts and, consequently, the potential use of the system in a clinical environment.
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