Deep learning based gastric cancer identification

Yuexiang Li, Xuechen Li, Xinpeng Xie, Linlin Shen

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

48 Citations (Scopus)


Gastric cancer is one of the most common cancers, which causes the second largest deaths worldwide. Manual pathological inspection of gastric slice is time-consuming and usually suffers from inter-observer variations. In this paper, we proposed a deep learning based framework, namely GastricNet, for automatic gastric cancer identification. The proposed network adopts different architectures for shallow and deep layers for better feature extraction. We evaluate the proposed framework on publicly available BOT gastric slice dataset. The experimental results show that our deep learning framework performs better than state-of-the-art networks like DenseNet, ResNet, and achieved an accuracy of 100% for slice-based classification.

Original languageEnglish
Title of host publication2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018
PublisherIEEE Computer Society
Number of pages4
ISBN (Electronic)9781538636367
Publication statusPublished - 23 May 2018
Externally publishedYes
Event15th IEEE International Symposium on Biomedical Imaging, ISBI 2018 - Washington, United States
Duration: 4 Apr 20187 Apr 2018

Publication series

NameProceedings - International Symposium on Biomedical Imaging
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452


Conference15th IEEE International Symposium on Biomedical Imaging, ISBI 2018
Country/TerritoryUnited States


  • Classification
  • Deep learning network
  • Gastric cancer

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging


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