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https://www.selleckchem.com/products/rxc004.html net.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. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.BACKGROUND In patients with Alzheimer's disease (AD), cerebral blood flow (CBF) is decreased from the early stages. CBF in AD is currently estimated from Z-scores using statistical analysis. However, the Z-score is not considered the impaired area ratio. METHODS In the present study, a novel indicator, ΣzS, associated with brain surface area and Z-scores, was defined and

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