  Pneumothorax, a collapsed or dropped lung, is a fatal condition typically
detected on a chest X-ray by an experienced radiologist. Due to shortage of
such experts, automated detection systems based on deep neural networks have
been developed. Nevertheless, applying such systems in practice remains a
challenge. These systems, mostly compute a single probability as output, may
not be enough for diagnosis. On the contrary, content-based medical image
retrieval (CBIR) systems, such as image search, can assist clinicians for
diagnostic purposes by enabling them to compare the case they are examining
with previous (already diagnosed) cases. However, there is a lack of study on
such attempt. In this study, we explored the use of image search to classify
pneumothorax among chest X-ray images. All chest X-ray images were first tagged
with deep pretrained features, which were obtained from existing deep learning
models. Given a query chest X-ray image, the majority voting of the top K
retrieved images was then used as a classifier, in which similar cases in the
archive of past cases are provided besides the probability output. In our
experiments, 551,383 chest X-ray images were obtained from three large recently
released public datasets. Using 10-fold cross-validation, it is shown that
image search on deep pretrained features achieved promising results compared to
those obtained by traditional classifiers trained on the same features. To the
best of knowledge, it is the first study to demonstrate that deep pretrained
features can be used for CBIR of pneumothorax in half a million chest X-ray
images.
