Computer-aided diagnosis system for ulcer detection in wireless capsule endoscopy images

Abstract

Diseases of the digestive tract, such as ulcers, pose a serious threat to human health. In fact, many types of endoscopy are employed to examine the patient's gastrointestinal tract. Recently, wireless capsule endoscopy (WCE) is presented as an excellent diagnostic tool for evaluation of gastrointestinal diseases compared to traditional endoscopies. This diagnosis usually takes a long time, which is tiring, and so the doctors may miss parts where abnormalities of the gastrointestinal tract may present. Therefore, automated diagnostic technics to detect symptoms of gastrointestinal illness in WCE images is adopted as an excellent enhancement tool for these doctors. In this work, a new computer-aided diagnosis method for ulcer detection in WCE images is proposed. After a preprocessing step, fine-tuned convolutional neural network (CNN) is used to extract deep features from these images. Since the number of ulcer images in the available data sets is limited, the CNN networks used in this work were pre-trained on millions of labeled natural images (ImageNet). After the deep features extraction, a random forest classifier is employed to detect ulcer from WCE images. The proposed approach demonstrates promising results (96.73 % and 95.34 % in terms of precision and recall respectively). Those results are satisfactory when compared to recent state-of-the-art methods.