Noora Mazin, Sheet and Ziyad K., Farej and Amer Farhan, Sheet (2024) A Survey on Techniques of Wireless Capsule Endoscopy for Image Enhancement and Disease Detection. European Journal of Computer Science and Information Technology, 12 (2). pp. 65-74. ISSN 2054-0957 (Print), 2054-0965 (Online)
![[thumbnail of A Survey on Techniques of Wireless Capsule Endoscopy.pdf]](https://tudr.org/style/images/fileicons/text.png)
Text
A Survey on Techniques of Wireless Capsule Endoscopy.pdf - Published Version
Restricted to Registered users only
Available under License Creative Commons Attribution Non-commercial No Derivatives.
Download (312kB) | Request a copy
Abstract
Wireless capsule endoscopy (WCE) is the gold standard for diagnosing small bowel disorders and is considered the future of effective diagnostic gastrointestinal (GI) endoscopy. Patients find it comfortable and more likely to adopt it than traditional colonoscopy and gastroscopy, making it a viable option for detecting cancer or ulcerations. WCE can obtain images of the GI tract from the inside, but pinpointing the disease's location remains a challenge. This paper reviews studies on endoscopy capsule development and discusses techniques and solutions for higher efficiency. Research has demonstrated that artificial intelligence (AI) enhances the accuracy of disease detection and minimizes errors resulting from physicians' fatigue or lack of attention. When it comes to WCE, deep learning has shown remarkable success in detecting a wide variety of disorders.
| Item Type: | Article |
|---|---|
| Subjects: | T Technology > T Technology (General) |
| Depositing User: | Professor Mark T. Owen |
| Date Deposited: | 01 May 2024 12:51 |
| Last Modified: | 01 May 2024 12:51 |
| URI: | https://tudr.org/id/eprint/2931 |
Actions (login required)
- View Item
CORE (COnnecting REpositories)
CORE (COnnecting REpositories)