A comprehensive analysis of classification methods in gastrointestinal endoscopy imaging

Debesh Jha*, Sharib Ali, Steven Hicks, Vajira Thambawita, Hanna Borgli, Pia H. Smedsrud, Thomas de Lange, Konstantin Pogorelov, Xiaowei Wang, Philipp Harzig, Minh Triet Tran, Wenhua Meng, Trung Hieu Hoang, Danielle Dias, Tobey H. Ko, Taruna Agrawal, Olga Ostroukhova, Zeshan Khan, Muhammad Atif Tahir, Yang LiuYuan Chang, Mathias Kirkerød, Dag Johansen, Mathias Lux, Håvard D. Johansen, Michael A. Riegler, Pål Halvorsen

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

18 Citations (Scopus)


Gastrointestinal (GI) endoscopy has been an active field of research motivated by the large number of highly lethal GI cancers. Early GI cancer precursors are often missed during the endoscopic surveillance. The high missed rate of such abnormalities during endoscopy is thus a critical bottleneck. Lack of attentiveness due to tiring procedures, and requirement of training are few contributing factors. An automatic GI disease classification system can help reduce such risks by flagging suspicious frames and lesions. GI endoscopy consists of several multi-organ surveillance, therefore, there is need to develop methods that can generalize to various endoscopic findings. In this realm, we present a comprehensive analysis of the Medico GI challenges: Medical Multimedia Task at MediaEval 2017, Medico Multimedia Task at MediaEval 2018, and BioMedia ACM MM Grand Challenge 2019. These challenges are initiative to set-up a benchmark for different computer vision methods applied to the multi-class endoscopic images and promote to build new approaches that could reliably be used in clinics. We report the performance of 21 participating teams over a period of three consecutive years and provide a detailed analysis of the methods used by the participants, highlighting the challenges and shortcomings of the current approaches and dissect their credibility for the use in clinical settings. Our analysis revealed that the participants achieved an improvement on maximum Mathew correlation coefficient (MCC) from 82.68% in 2017 to 93.98% in 2018 and 95.20% in 2019 challenges, and a significant increase in computational speed over consecutive years.

Original languageEnglish
Article number102007
JournalMedical Image Analysis
Publication statusPublished - May 2021

Scopus Subject Areas

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Computer Vision and Pattern Recognition
  • Health Informatics
  • Computer Graphics and Computer-Aided Design

User-Defined Keywords

  • Artificial intelligence
  • BioMedia 2019 grand challenge
  • Computer-aided detection and diagnosis
  • Gastrointestinal endoscopy challenges
  • Medical imaging
  • Medico Task 2017
  • Medico Task 2018


Dive into the research topics of 'A comprehensive analysis of classification methods in gastrointestinal endoscopy imaging'. Together they form a unique fingerprint.

Cite this