Today's radiologists are highly concerned with motion artifact available in MR images and expect MR images during diagnosis without motion artifact. Radiologists receive merged data of MR images with and without motion at the time for patient diagnosis. The radiologist may be mislead by these images of motion artifacts. We suggest a classification model in this paper that makes use of a deep autoencoder convolutional neural network (DAE-CNN) and has three levels as (i) input level (ii) hidden level and (iii) output level. We assessed the classification model and the results are analyzed regarding the accuracy in terms of motion/without motion MR Images.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.