Deep Learning Features for Wireless Capsule Endoscopy Analysis

Seguí, Santi; Drozdzal, Michal; Pascual, Guillem; Radeva, Petia; Malagelada, Carolina; Azpiroz, Fernando; Vitrià, Jordi

doi:10.1007/978-3-319-52277-7_40

Cited by 2 publications

(1 citation statement)

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“…Moreover, the variation in the distance between the intestinal surface and the capsule leads to the intensity dissimilarity in the WCE images, thus, the HS histograms have been applied [19]. Moreover, Seguí et al [22] recommended and implemented a deep learning model for extracting generic feature descriptor from the WCE images.…”

Section: Wce Image Features Extractionmentioning

confidence: 99%

Colored Video Analysis in Wireless Capsule Endoscopy: A Survey of State-of-the-Art

Ashour

Dey

Mohamed

et al. 2020

CMIR

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: Wireless Capsule Endoscopy (WCE) is a highly promising technology for gastrointestinal (GI) tract abnormality diagnosis. However, low image resolution and low frame rates are challenging issues in WCE. In addition, the relevant frames containing the features of interest for accurate diagnosis only constitute 1% of the complete video information. For these reasons, analyzing the WCE videos is still a time consuming and laborious examination for the gastroenterologists, which reduces WCE system usability. This leads to the emergent need to speed-up and automates the WCE video process for GI tract examinations. Consequently, the present work introduced the concept of WCE technology, including the structure of WCE systems, with a focus on the medical endoscopy video capturing process using image sensors. It discussed also the significant characteristics of the different GI tract for effective feature extraction. Furthermore, video approaches for bleeding and lesion detection in the WCE video were reported with computer-aided diagnosis systems in different applications to support the gastroenterologist in the WCE video analysis. In image enhancement, WCE video review time reduction is also discussed, while reporting the challenges and future perspectives, including the new trend to employ the deep learning models for feature Learning, polyp recognition, and classification, as a new opportunity for researchers to develop future WCE video analysis techniques.

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Section: Wce Image Features Extractionmentioning

confidence: 99%