  Wireless capsule endoscopy (WCE) is an effective mean for diagnosis of
gastrointestinal disorders. Detection of informative scenes in WCE video could
reduce the length of transmitted videos and help the diagnosis procedure. In
this paper, we investigate the problem of simplification of neural networks for
automatic bleeding region detection inside capsule endoscopy device. Suitable
color channels are selected as neural networks inputs, and image classification
is conducted using a multi-layer perceptron (MLP) and a convolutional neural
network (CNN) separately. Both CNN and MLP structures are simplified to reduce
the number of computational operations. Performances of two simplified networks
are evaluated on a WCE bleeding image dataset using the DICE score. Simulation
results show that applying simplification methods on both MLP and CNN
structures reduces the number of computational operations significantly with
AUC greater than 0.97. Although CNN performs better in comparison with
simplified MLP, the simplified MLP segments bleeding regions with a
significantly smaller number of computational operations. Concerning the
importance of having a simple structure or a more accurate model, each of the
designed structures could be selected for inside capsule implementation.
