European Journal of Computer Science and Information Technology (EJCSIT)

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A Survey on Techniques of Wireless Capsule Endoscopy for Image Enhancement and Disease Detection


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.

Keywords: CNN, Location, bowel, detection, wireless capsule endoscopy

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This work by European American Journals is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 4.0 Unported License


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Print ISSN: 2054-0957
Online ISSN: 2054-0965

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