Results 61 to 70 of about 582 (150)

Capsule endoscopy in clinical practice: concise up-to-date overview

Clinical and Experimental Gastroenterology, 2009
Anastasios Koulaouzidis, Sarah DouglasCentre for Liver and Digestive Disorders, Royal Infirmary of Edinburgh, Edinburgh, UKAbstract: Until recently, the small bowel was considered a ‘no man’s land’ as the imaging modalities ...
Anastasios Koulaouzidis, Sarah Douglas
doaj  

State of the Art on Advancements in Wireless Capsule Endoscopy Telemetry: A Systematic Approach

IEEE Open Journal of Antennas and Propagation
In the last decades an innovative technique has emerged in clinical gastroenterology as a compelling alternative to the traditional wired endoscopy, known as Wireless Capsule Endoscopy (WCE).
Sara Fontana, Simona D'Agostino, Alessandra Paffi, Paolo Marracino, Marco Balucani, Giancarlo Ruocco, Salvatore Maria Aglioti, Francesca Apollonio, Micaela Liberti   +8 more
doaj   +1 more source

Feature Selection and Classification of Ulcerated Lesions Using Statistical Analysis for WCE Images

Applied Sciences, 2017
Wireless capsule endoscopy (WCE) is a technology developed to inspect the whole gastrointestinal tract (especially the small bowel area that is unreachable using the traditional endoscopy procedure) for various abnormalities in a non-invasive manner ...
Shipra Suman, Fawnizu Azmadi Hussin, Aamir Saeed Malik, Shiaw Hooi Ho, Ida Hilmi, Alex Hwong-Ruey Leow, Khean-Lee Goh   +6 more
doaj   +1 more source

Small Bowel Obstruction with Perforation Secondary to PillCam

Case Reports in Gastrointestinal Medicine, 2018
We present a case of a 63-year-old female who presented with small bowel obstruction and perforation secondary to a retained PillCam®. She initially presented with iron-deficiency anaemia (haemoglobin 44 g/L, ferritin 190 mcg/L). After unremarkable upper
Toan Pham, Ashley Miller, Domenic La Paglia, Alvin Cham   +3 more
doaj   +1 more source

Application of Convolutional Neural Networks for Automated Ulcer Detection in Wireless Capsule Endoscopy Images

Sensors, 2019
Detection of abnormalities in wireless capsule endoscopy (WCE) images is a challenging task. Typically, these images suffer from low contrast, complex background, variations in lesion shape and color, which affect the accuracy of their segmentation and ...
Haya Alaskar, Abir Hussain, Nourah Al-Aseem, Panos Liatsis, Dhiya Al-Jumeily   +4 more
doaj   +1 more source

Ultra-Wideband Antennas for Wireless Capsule Endoscope System: A Review

IEEE Open Journal of Antennas and Propagation
Wireless capsule endoscopy (WCE) systems are used to capture images of the human digestive tract for medical applications, especially the noninvasive diagnostics and treatments of small intestinal diseases.
Ben Li, Yifan Wang, Junchi Zhao, Jingjing Shi   +3 more
doaj   +1 more source

Polypoid Lesion Segmentation Using YOLO-V8 Network in Wireless Video Capsule Endoscopy Images

Diagnostics
Gastrointestinal (GI) tract disorders are a significant public health issue. They are becoming more common and can cause serious health problems and high healthcare costs.
Ali Sahafi, Anastasios Koulaouzidis, Mehrshad Lalinia   +2 more
doaj   +1 more source

WIRELESS CAPSULE ENDOSCOPY (WCE) IN PEDIATRIC PATIENTS

Journal of Pediatric Gastroenterology and Nutrition, 2006
Rula Harb, Chuan‐Hao Lin, Hillel Naon
openaire   +2 more sources

An Accurate Deep Learning-Based Computer-Aided Diagnosis System for Gastrointestinal Disease Detection Using Wireless Capsule Endoscopy Image Analysis

Applied Sciences
Peptic ulcers and stomach cancer are common conditions that impact the gastrointestinal (GI) system. Wireless capsule endoscopy (WCE) has emerged as a widely used, noninvasive technique for diagnosing these issues, providing valuable insights through the
Sameh Abd El-Ghany, Mahmood A. Mahmood, A. A. Abd El-Aziz   +2 more
doaj   +1 more source

Uncertainty-driven active learning in a deep semi-supervised framework for WCE image classification

Results in Engineering
Wireless Capsule Endoscopy (WCE) image classification using deep learning models is hindered by data scarcity and model uncertainty. Labelling medical images is costly and time-consuming, limiting the availability of labelled data for training.
Prabhanantha Kumar Muruganantham, Senthil Murugan Balakrishnan   +1 more
doaj   +1 more source