  The interpretation and analysis of the wireless capsule endoscopy recording
is a complex task which requires sophisticated computer aided decision (CAD)
systems in order to help physicians with the video screening and, finally, with
the diagnosis. Most of the CAD systems in the capsule endoscopy share a common
system design, but use very different image and video representations. As a
result, each time a new clinical application of WCE appears, new CAD system has
to be designed from scratch. This characteristic makes the design of new CAD
systems a very time consuming. Therefore, in this paper we introduce a system
for small intestine motility characterization, based on Deep Convolutional
Neural Networks, which avoids the laborious step of designing specific features
for individual motility events. Experimental results show the superiority of
the learned features over alternative classifiers constructed by using state of
the art hand-crafted features. In particular, it reaches a mean classification
accuracy of 96% for six intestinal motility events, outperforming the other
classifiers by a large margin (a 14% relative performance increase).
