Computer-Aided Diagnosis for Capsule Endoscopy: From Inception to Future
Kuntesh K. Jani1, Subodh Srivastava2, Rajeev Srivastava3
1Kuntesh K. Jani*, Computer Science and Engineering Department, Indian Institute of Technology (Banaras Hindu University) Varanasi, Uttar Pradesh, India.
2Subodh Srivastava, Electronics and Communication Engineering Department, National Institute of Technology Patna, India.
3Rajeev Srivastava Computer Science and Engineering Department, Indian Institute of Technology (Banaras Hindu University) Varanasi.
Manuscript received on November 17., 2019. | Revised Manuscript received on November 24 2019. | Manuscript published on 30 November, 2019. | PP: 12261-12273 | Volume-8 Issue-4, November 2019. | Retrieval Number: D8094118419/2019©BEIESP | DOI: 10.35940/ijrteD8094.118419
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Background: Gastrointestinal (GI) tract abnormalities are most common across the world, and it is a significant threat to the health of human beings. Capsule endoscopy is a non-sedative, non-invasive and patient-friendly procedure for the diagnosis of GI tract abnormalities. However, it is very time consuming and tiresome task for physicians due to length of endoscopy videos. Thus computer-aided diagnosis (CAD) system is a must. Methods: This systematic review aims to investigate state-of-the-art CAD systems for automatic abnormality detection in capsule endoscopy by examining publications from scientific databases namely IEEE Xplore, Science Direct, Springer, and Scopus. Results: Based on defined search criteria and applied inclusion and exclusion criteria, 44 articles are included out of 187. This study presents the current status and analysis of CAD systems for capsule endoscopy. Conclusion: Publicly available larger dataset and a deep learning based CAD system may help to improve the efficiency of automated abnormality detection in capsule endoscopy.
Keywords: Capsule Endoscopy, Automated Abnormality Detection, Computer-Aided Diagnosis.
Scope of the Article: Advanced Computer Networking.