The Effectiveness of Data Augmentation for Detection of Gastrointestinal Diseases from Endoscopical Images

Andrea Asperti, Claudio Mastronardo

2018

Abstract

The lack, due to privacy concerns, of large public databases of medical pathologies is a well-known and major problem, substantially hindering the application of deep learning techniques in this field. In this article, we investigate the possibility to supply to the deficiency in the number of data by means of data augmentation techniques, working on the recent Kvasir dataset (Pogorelov et al., 2017) of endoscopical images of gastrointestinal diseases. The dataset comprises 4,000 colored images labeled and verified by medical endoscopists, covering a few common pathologies at different anatomical landmarks: Z-line, pylorus and cecum. We show how the application of data augmentation techniques allows to achieve sensible improvements of the classification with respect to previous approaches, both in terms of precision and recall.

Paper Citation

in Harvard Style

Asperti A. and Mastronardo C. (2018). The Effectiveness of Data Augmentation for Detection of Gastrointestinal Diseases from Endoscopical Images.In Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: KALSIMIS, ISBN 978-989-758-278-3, pages 199-205. DOI: 10.5220/0006730901990205

in Bibtex Style

@conference{kalsimis18,
author={Andrea Asperti and Claudio Mastronardo},
title={The Effectiveness of Data Augmentation for Detection of Gastrointestinal Diseases from Endoscopical Images},
booktitle={Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: KALSIMIS,},
year={2018},
pages={199-205},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006730901990205},
isbn={978-989-758-278-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: KALSIMIS,
TI - The Effectiveness of Data Augmentation for Detection of Gastrointestinal Diseases from Endoscopical Images
SN - 978-989-758-278-3
AU - Asperti A.
AU - Mastronardo C.
PY - 2018
SP - 199
EP - 205
DO - 10.5220/0006730901990205