Gastrointestinal bleeding detection in wireless capsule endoscopy images using handcrafted and CNN features

Gastrointestinal (GI) bleeding detection plays an essential role in wireless capsule endoscopy (WCE) examination. In this paper, we present a new approach for WCE bleeding detection that combines handcrafted (HC) features and convolutional neural network (CNN) features. Compared with our previous work, a smaller-scale CNN architecture is constructed to lower the computational cost. In experiments, we show that the proposed strategy is highly capable when training data is limited, and yields comparable or better results than the latest methods.