Generic feature learning for wireless capsule endoscopy analysis

Santi Seguí, Michal Drozdzal, Guillem Pascual, Petia Radeva, Carolina Malagelada, Fernando Azpiroz, Jordi Vitrià

Research output: Contribution to journalArticleResearchpeer-review


The interpretation and analysis of wireless capsule endoscopy (WCE) recordings is a complex task which requires sophisticated computer aided decision (CAD) systems to help physicians with video screening and, finally, with the diagnosis. Most CAD systems used in capsule endoscopy share a common system design, but use very different image and video representations. As a result, each time a new clinical application of WCE appears, a new CAD system has to be designed from the scratch. This makes the design of new CAD systems very time consuming. Therefore, in this paper we introduce a system for small intestine motility characterization, based on Deep Convolutional Neural Networks, which circumvents the laborious step of designing specific features for individual motility events. Experimental results show the superiority of the learned features over alternative classifiers constructed using state-of-the-art handcrafted features. In particular, it reaches a mean classification accuracy of 96% for six intestinal motility events, outperforming the other classifiers by a large margin (a 14% relative performance increase).

Original languageEnglish
Pages (from-to)163-172
Number of pages10
JournalComputers in Biology and Medicine
Publication statusPublished - 1 Dec 2016


  • Algorithms
  • Capsule Endoscopy/methods
  • Gastrointestinal Motility/physiology
  • Humans
  • Image Processing, Computer-Assisted/methods
  • Machine Learning


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