Automatic detection of early esophageal cancer with CNNS using transfer learning

Sjors van Riel, Fons van der Sommen, Sveta Zinger, Erik J. Schoon, Peter H.N. de With

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

20 Citations (Scopus)
3 Downloads (Pure)


The incidence of Esophageal Adenocarcinoma (EAC), a form of esophageal cancer, has rapidly increased in recent years. Dysplastic tissue can be removed endoscopically at an early stage, and since survival chances of patients are limited at later stages of the disease, early detection is of key impor- tance. Recently, several CAD systems for HD endoscopic images have been proposed, but these are computationally expensive, making them unfit for clinical use requiring real- time analysis. In this paper, we present a novel approach for early esophageal cancer detection using Transfer Learning with CNNs. Given the small amount of annotated data, CNN Codes are applied, where intermediate layers of the net- work are used as features for conventional classifiers. Various classifiers are combined with four of the most widely-used networks. Additionally, sliding windows are used to obtain a coarse-grained annotation indicating any possible cancerous regions. This approach outperforms the current state-of-the-art with a frame-based AUC of 0.92, while allowing both near real-time prediction and annotation at 2 fps, in a MATLAB-based framework.
Original languageEnglish
Title of host publication2018 IEEE International Conference on Image Processing, ICIP 2018 - Proceedings
Place of PublicationPiscataway
PublisherIEEE Computer Society
Number of pages5
ISBN (Electronic)978-1-4799-7061-2
ISBN (Print)978-1-4799-7062-9
Publication statusPublished - Oct 2018
Event25th IEEE International Conference on Image Processing (ICIP 2018) - Megaron Athens International Conference Centre, Athens, Greece
Duration: 7 Oct 201810 Oct 2018
Conference number: 25


Conference25th IEEE International Conference on Image Processing (ICIP 2018)
Abbreviated titleICIP 2018
Internet address


  • CNNs
  • Computer-aided diagnosis
  • Esophageal cancer
  • Transfer learning


Dive into the research topics of 'Automatic detection of early esophageal cancer with CNNS using transfer learning'. Together they form a unique fingerprint.

Cite this