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Wireless capsule endoscopy (WCE) is an effective mean for diagnosis ofgastrointestinal disorders. Detection of informative scenes in WCE video couldreduce the length of transmitted videos and help the diagnosis procedure. Inthis paper, we investigate the problem of simplification of neural networks forautomatic bleeding region detection inside capsule endoscopy device. Suitablecolor channels are selected as neural networks inputs, and image classificationis conducted using a multi-layer perceptron (MLP) and a convolutional neuralnetwork (CNN) separately. Both CNN and MLP structures are simplified to reducethe number of computational operations. Performances of two simplified networksare evaluated on a WCE bleeding image dataset using the DICE score. Simulationresults show that applying simplification methods on both MLP and CNNstructures reduces the number of computational operations significantly withAUC greater than 0.97. Although CNN performs better in comparison withsimplified MLP, the simplified MLP segments bleeding regions with asignificantly smaller number of computational operations. Concerning theimportance of having a simple structure or a more accurate model, each of thedesigned structures could be selected for inside capsule implementation.

Segmentation of Bleeding Regions in Wireless Capsule Endoscopy for Detection of Informative Frames