High accuracy patch-level classification of wireless capsule endoscopy images using a convolutional neural network

Wireless capsule endoscopy (WCE) is a technology used to record colored internal images of the gastrointestinal (GI) tract for the purpose of medical diagnosis. It transmits a large number of frames in a single examination cycle, which makes the process of analyzing and diagnosis of abnormalities extremely challenging and time-consuming. In this paper, we propose a technique to automate the abnormality detection in WCE images following a deep learning approach. The WCE images are split into patches and input to a convolutional neural network (CNN). A trained deep neural network is used to classify patches to be either malign or benign. The patches with abnormalities are marked on the WCE image output. We obtained an area under receiver-operating-characteristic curve (AUROC) value of about 98.65% on a publicly available test data containing nine abnormalities.