A Deep Convolutional Neural Network for Bleeding Detection in Wireless Capsule Endoscopy Images
Refereed conference paper presented and published in conference proceedings


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AbstractWireless Capsule Endoscopy (WCE) is a standard non-invasive modality for small bowel examination. Recently, the development of computer-aided diagnosis (CAD) systems for gastrointestinal (GI) bleeding detection in WCE image videos has become an active research area with the goal of relieving the workload of physicians. Existing methods based primarily on handcrafted features usually give insufficient accuracy for bleeding detection, due to their limited capability of feature representation. In this paper, we present a new automatic bleeding detection strategy based on a deep convolutional neural network and evaluate our method on an expanded dataset of 10,000 WCE images. Experimental results with an increase of around 2 percentage points in the F-1 score demonstrate that our method outperforms the state-of-the-art approaches in WCE bleeding detection. The achieved F-1 score is of up to 0.9955.
All Author(s) ListJia X, Meng MQH
Name of Conference2016 IEEE 38th Annual International Conference of the Engineering in Medicine and Biology Society (EMBC)
Start Date of Conference16/08/2016
End Date of Conference20/08/2016
Place of ConferenceOrlando, FL, USA
Country/Region of ConferenceUnited States of America
Proceedings Title2016 IEEE 38th Annual International Conference of the Engineering in Medicine and Biology Society (EMBC)
Title of Publication2016 38TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC)
Year2016
Month8
Pages639 - 642
ISBN978-1-4577-0219-8
eISBN978-1-4577-0220-4
ISSN1557-170X
eISSN1558-4615
LanguagesEnglish-United Kingdom
Web of Science Subject CategoriesEngineering, Biomedical;Engineering, Electrical & Electronic;Engineering

Last updated on 2020-18-09 at 02:40