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Ensemble of Deep Convolutional Neural Networks for Classification of Early Barrett’s Neoplasia Using Volumetric Laser Endomicroscopy

Authors :
Roger Fonollà
Thom Scheeve
Maarten R. Struyvenberg
Wouter L. Curvers
Albert J. de Groof
Fons van der Sommen
Erik J. Schoon
Jacques J.G.H.M. Bergman
Peter H.N. de With
Source :
Applied Sciences, Vol 9, Iss 11, p 2183 (2019)
Publication Year :
2019
Publisher :
MDPI AG, 2019.

Abstract

Barrett’s esopaghagus (BE) is a known precursor of esophageal adenocarcinoma (EAC). Patients with BE undergo regular surveillance to early detect stages of EAC. Volumetric laser endomicroscopy (VLE) is a novel technology incorporating a second-generation form of optical coherence tomography and is capable of imaging the inner tissue layers of the esophagus over a 6 cm length scan. However, interpretation of full VLE scans is still a challenge for human observers. In this work, we train an ensemble of deep convolutional neural networks to detect neoplasia in 45 BE patients, using a dataset of images acquired with VLE in a multi-center study. We achieve an area under the receiver operating characteristic curve (AUC) of 0.96 on the unseen test dataset and we compare our results with previous work done with VLE analysis, where only AUC of 0.90 was achieved via cross-validation on 18 BE patients. Our method for detecting neoplasia in BE patients facilitates future advances on patient treatment and provides clinicians with new assisting solutions to process and better understand VLE data.

Details

Language :
English
ISSN :
20763417
Volume :
9
Issue :
11
Database :
Directory of Open Access Journals
Journal :
Applied Sciences
Publication Type :
Academic Journal
Accession number :
edsdoj.54b41933811b42ef8c67aec6e86532e5
Document Type :
article
Full Text :
https://doi.org/10.3390/app9112183