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Deep learning in gastric tissue diseases: a systematic review
  1. Wanderson Gonçalves e Gonçalves1,2,
  2. Marcelo Henrique de Paula dos Santos3,
  3. Fábio Manoel França Lobato4,
  4. Ândrea Ribeiro-dos-Santos1,2,
  5. Gilderlanio Santana de Araújo1
  1. 1Laboratório de Genética Humana e Médica - Instituto de Ciências Biológicas, Universidade Federal do Pará, Belém, Pará, Brazil
  2. 2Núcleo de Pesquisas em Oncologia, Universidade Federal do Pará, Belém, Pará, Brazil
  3. 3Engenharia da Computação, Universidade Federal do Pará, Belém, Pará, Brazil
  4. 4Instituto de Engenharia e Geociências, Universidade Federal do Oeste do Pará, Santarém, Pará, Brazil
  1. Correspondence to Dr Gilderlanio Santana de Araújo; gilderlanio{at}


Background In recent years, deep learning has gained remarkable attention in medical image analysis due to its capacity to provide results comparable to specialists and, in some cases, surpass them. Despite the emergence of deep learning research on gastric tissues diseases, few intensive reviews are addressing this topic.

Method We performed a systematic review related to applications of deep learning in gastric tissue disease analysis by digital histology, endoscopy and radiology images.

Conclusions This review highlighted the high potential and shortcomings in deep learning research studies applied to gastric cancer, ulcer, gastritis and non-malignant diseases. Our results demonstrate the effectiveness of gastric tissue analysis by deep learning applications. Moreover, we also identified gaps of evaluation metrics, and image collection availability, therefore, impacting experimental reproducibility.

  • image analysis
  • gastric diseases
  • medical decision analysis

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See:

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  • Contributors WGG, MHPS and GSA wrote the paper. WGG and GSA designed the research. WGG and MHPS collected the data. WGG, MHPS and GSA analysed the data. FL and ARS reviewed the paper. WGG, MHPS, FL, ARS and GSA agree with manuscript results and conclusions.

  • Funding Fundação Amazônia Paraense de Amparo à Pesquisa – FAPESPA (No. 008/2017) and PROPESP/UFPA for the financial support and scholarships.

  • Competing interests None declared.

  • Patient consent for publication Not required.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Data availability statement All data relevant to the study are included in the article.

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