  Content-based image retrieval (CBIR) in large medical image archives is a
challenging and necessary task. Generally, different feature extraction methods
are used to assign expressive and invariant features to each image such that
the search for similar images comes down to feature classification and/or
matching. The present work introduces a new image retrieval method for medical
applications that employs a convolutional neural network (CNN) with recently
introduced Radon barcodes. We combine neural codes for global classification
with Radon barcodes for the final retrieval. We also examine image search based
on regions of interest (ROI) matching after image retrieval. The IRMA dataset
with more than 14,000 x-rays images is used to evaluate the performance of our
method. Experimental results show that our approach is superior to many
published works.
