Abstract
Wireless capsule endoscopy (WCE) is a novel imaging technique that can view the entire small bowel in human body. Thus, it is presented as an excellent diagnostic tool for evaluation of gastrointestinal diseases compared with traditional endoscopies. However, the diagnosis by the physicians is tedious since it requires reviewing the video extracted from the capsule and analysing all of its frames. This tedious task has encouraged the researchers to provide automated diagnostic technics for WCE frameworks to detect symptoms of gastrointestinal illness. In this paper, we present the prelimenary results of red lesion detection in WCE images using Dense-Unet deep learning segmentation model. To this end, we have used a dataset containing two subsets of anonymized video capsule endoscopy images with annotated red lesions. The first set, used in this work, has 3,295 non-sequential frames and their corresponding annotated masks. The results obtained by the proposed scheme are promising.
Highlights
We present the prelimenary results of red lesion detection in Wireless capsule endoscopy (WCE) images using Dense-Unet deep learning segmentation model
Diseases of the digestive tract, such as esophagus, stomach and small intestine, colon, and other digestive organs cancers pose a serious threat to human health
Capsule endoscopy is mainly used to find the cause of unexplained bleeding in the digestive tract or in case of inflammation, red lesion or tumors in the small intestine
Summary
Diseases of the digestive tract, such as esophagus, stomach and small intestine, colon, and other digestive organs cancers pose a serious threat to human health. A study in [12], proposed a novel method that is based on CNN to automatically recognize polyp in small bowel WCE image Training a CNN model, from scratch, for WCE images classification was introduced by researchers in [17] Their method aims to multi diseases detection i.e., normal mucosa, bile predominant, air bubbles, debris, inflamed mucosa, a typical vascularity, and bleeding. A framework for best features selection was introduced in [20], the authors presented a fully automated system for stomach infection recognition based on deep learning features fusion and selection In this design, ulcer images are assigned manually and support to a saliency-based method for ulcer detection.
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