  Knowing the type (i.e., the biochemical composition) of kidney stones is
crucial to prevent relapses with an appropriate treatment. During
ureteroscopies, kidney stones are fragmented, extracted from the urinary tract,
and their composition is determined using a morpho-constitutional analysis.
This procedure is time consuming (the morpho-constitutional analysis results
are only available after some days) and tedious (the fragment extraction lasts
up to an hour). Identifying the kidney stone type only with the in-vivo
endoscopic images would allow for the dusting of the fragments, while the
morpho-constitutional analysis could be avoided. Only few contributions dealing
with the in vivo identification of kidney stones were published. This paper
discusses and compares five classification methods including deep convolutional
neural networks (DCNN)-based approaches and traditional (non DCNN-based) ones.
Even if the best method is a DCCN approach with a precision and recall of 98%
and 97% over four classes, this contribution shows that a XGBoost classifier
exploiting well-chosen feature vectors can closely approach the performances of
DCNN classifiers for a medical application with a limited number of annotated
data.
