Title page for etd-0803118-143818


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URN etd-0803118-143818
Author Yi-Fan Lin
Author's Email Address No Public.
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Department Electrical Engineering
Year 2018
Semester 1
Degree Master
Type of Document
Language zh-TW.Big5 Chinese
Title A Generative Collaborative Network for Detection of Polyps in Endoscopic Images
Date of Defense 2018-08-27
Page Count 62
Keyword
  • Polyps Detection
  • Double Network
  • Transpose Convolution Neural Network
  • Neural Network
  • Convolution Neural Network
  • Abstract Cancer is the leading cause of death in Taiwan and colorectal cancer ranks first in the top 10 cancers of the years several times. The effects of colorectal cancer can not be ignored, so the sooner the signs of colorectal cancer are detected, the greater the prospect of a cure.
    Therefore, we proposed a neural network based architecture for detecting the polyps in the large intestine endoscope. The proposed network architecture is named Generative Collaborative Network, consisting of two subnets: Generator and Collaborator. Two subnets are based on combination of Convolution Neural Network and Transpose Convolution Neural Network. The entire network learns how to extract polyp features from the training data and then maps them back to endoscopics, where polyps are marked.
    The data used in this paper are from three databases: cvc-clinicdb, etis-laribpolypdb and cvc-endoscenestill. The endoscopics was sent into Generator to produce the predicted pattern of polyps markers, and then sent into Collaborator to obtain the final pattern of polyps markers. Experimental results indicate that the method presented in this paper performs best. At the end of the paper, a comparison between the results presented in this paper and the results presented in other ways will be shown, as well as a set of clinical trial results.
    Advisory Committee
  • Zu-Sheng Li - chair
  • Ming-Yi Zhu - co-chair
  • Hui-Yong Lin - co-chair
  • Yu-Ren Chen - co-chair
  • Kao-Shing Hwang - advisor
  • Files
  • etd-0803118-143818.pdf
  • Indicate in-campus at 5 year and off-campus access at 5 year.
    Date of Submission 2018-09-03

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