  Medical images can be a valuable resource for reliable information to support
medical diagnosis. However, the large volume of medical images makes it
challenging to retrieve relevant information given a particular scenario. To
solve this challenge, content-based image retrieval (CBIR) attempts to
characterize images (or image regions) with invariant content information in
order to facilitate image search. This work presents a feature extraction
technique for medical images using stacked autoencoders, which encode images to
binary vectors. The technique is applied to the IRMA dataset, a collection of
14,410 x-ray images in order to demonstrate the ability of autoencoders to
retrieve similar x-rays given test queries. Using IRMA dataset as a benchmark,
it was found that stacked autoencoders gave excellent results with a retrieval
error of 376 for 1,733 test images with a compression of 74.61%.
