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Artificial intelligence support for disease detection in wireless capsule endoscopy images of human large bowel
Abstract
In the work the chosen algorithms of disease recognition in endoscopy images were described and compared for theirs efficiency. The algorithms were estimated with regard to utility for application in computer system's support for digestive system's diagnostics. Estimations were achieved in an advanced testing environment, which was built with use of the large collection of endoscopy movies received from Medical University in Gdańsk. For classification of the endoscopy images the neural networks and SVM classifiers were used. Efficiency of classifiers was also compared. To achieve the best scores of efficiency in disease recognition, all of the algorithms' input parameters were optimized. In the summary scores of tests and the bests of algorithms were described.
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Details
- Category:
- Conference activity
- Type:
- publikacja w wydawnictwie zbiorowym recenzowanym (także w materiałach konferencyjnych)
- Title of issue:
- Proceedings of the 5th International conference of Young Scientists : Computer Science & Engineering 2011, Lviv, Ukraine 24-26 November 2011 strony 24 - 27
- Language:
- English
- Publication year:
- 2011
- Bibliographic description:
- Cychnerski J.: Artificial intelligence support for disease detection in wireless capsule endoscopy images of human large bowel// Proceedings of the 5th International conference of Young Scientists : Computer Science & Engineering 2011, Lviv, Ukraine 24-26 November 2011/ ed. O. Berezko. - Lviv Polytechnic National University. Lviv: Publishing House of Lviv Polytechnic National University, 2011, s.24-27
- Verified by:
- Gdańsk University of Technology
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