Gastric Section Detection Based on Decision Fusion of Convolutional Neural Networks

Ting Hsuan Lin, Chun Rong Huang, Hsiu Chi Cheng, Bor Shyang Sheu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

To provide accurate histological parameter assessment of each gastric section from endoscopic images, gastric sections need to be correctly identified in advance. In this paper, we propose a novel CNN based ensemble learning method to detect gastric sections from endoscopic images by fusing decisions of multiple convolutional neural network (CNN) models which provide initial decision probability of the endoscopic image. The decision probability is concatenated and classified by a decision fusion network to achieve effective and efficient gastric section detection. In the experiments, we compare the proposed method with state-of-The-Art CNN and CNN based ensemble learning methods and conclude that the proposed method owns the best testing accuracy.

Original languageEnglish
Title of host publicationBioCAS 2019 - Biomedical Circuits and Systems Conference, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509006175
DOIs
Publication statusPublished - 2019 Oct
Event2019 IEEE Biomedical Circuits and Systems Conference, BioCAS 2019 - Nara, Japan
Duration: 2019 Oct 172019 Oct 19

Publication series

NameBioCAS 2019 - Biomedical Circuits and Systems Conference, Proceedings

Conference

Conference2019 IEEE Biomedical Circuits and Systems Conference, BioCAS 2019
CountryJapan
CityNara
Period19-10-1719-10-19

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Biomedical Engineering
  • Electrical and Electronic Engineering
  • Instrumentation

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    Lin, T. H., Huang, C. R., Cheng, H. C., & Sheu, B. S. (2019). Gastric Section Detection Based on Decision Fusion of Convolutional Neural Networks. In BioCAS 2019 - Biomedical Circuits and Systems Conference, Proceedings [8919015] (BioCAS 2019 - Biomedical Circuits and Systems Conference, Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIOCAS.2019.8919015