WCE Abnormality Detection Based on Saliency and Adaptive Locality-Constrained Linear Coding

@article{Yuan2017WCEAD,
  title={WCE Abnormality Detection Based on Saliency and Adaptive Locality-Constrained Linear Coding},
  author={Yixuan Yuan and Baopu Li and Max Q.-H. Meng},
  journal={IEEE Transactions on Automation Science and Engineering},
  year={2017},
  volume={14},
  pages={149-159}
}
Wireless capsule endoscopy (WCE) has become a widely used diagnostic technique for the digestive tract, at the price of a large volume of data that needs to be analyzed. To tackle this problem, a new computer-aided system using novel features is proposed in this paper to classify WCE images automatically. In the feature learning stage, to obtain the representative visual words, we first calculate the color scale invariant feature transform from the bleeding, polyp, ulcer, and normal WCE image… CONTINUE READING

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Key Quantitative Results

  • The experimental results exhibit a promising overall recognition accuracy of 88.61%, validating the effectiveness of the proposed method.
  • The proposed method shows superior performance with an improvement of 4.70% from 2.60%, in accuracy for bleeding image detection, 20.90% from 7.69% in accuracy for polyp image detection, and 57% from 35.11% in ulcer detection accuracy compared with the published methods in [15] and [16], respectively.

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