AI Article Synopsis

  • The paper discusses a new method for detecting gastrointestinal bleeding using wireless capsule endoscopy (WCE) that integrates both handcrafted features and features from convolutional neural networks (CNNs).
  • The authors have developed a smaller CNN architecture to reduce computational costs while maintaining effectiveness.
  • Experimental results indicate that this new approach performs well even with limited training data, achieving results on par with or superior to current state-of-the-art methods.

Article Abstract

Gastrointestinal (GI) bleeding detection plays an essential role in wireless capsule endoscopy (WCE) examination. In this paper, we present a new approach for WCE bleeding detection that combines handcrafted (HC) features and convolutional neural network (CNN) features. Compared with our previous work, a smaller-scale CNN architecture is constructed to lower the computational cost. In experiments, we show that the proposed strategy is highly capable when training data is limited, and yields comparable or better results than the latest methods.

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Source
http://dx.doi.org/10.1109/EMBC.2017.8037526DOI Listing

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