Preprint Article Version 1 This version is not peer-reviewed

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

Version 1 : Received: 17 May 2019 / Approved: 20 May 2019 / Online: 20 May 2019 (11:50:09 CEST)

A peer-reviewed article of this Preprint also exists.

Fonollà, R.; Scheeve, T.; Struyvenberg, M.R.; Curvers, W.L.; de Groof, A.J.; van der Sommen, F.; Schoon, E.J.; Bergman, J.J.; de With, P.H. Ensemble of Deep Convolutional Neural Networks for Classification of Early Barrett’s Neoplasia Using Volumetric Laser Endomicroscopy. Appl. Sci. 2019, 9, 2183. Fonollà, R.; Scheeve, T.; Struyvenberg, M.R.; Curvers, W.L.; de Groof, A.J.; van der Sommen, F.; Schoon, E.J.; Bergman, J.J.; de With, P.H. Ensemble of Deep Convolutional Neural Networks for Classification of Early Barrett’s Neoplasia Using Volumetric Laser Endomicroscopy. Appl. Sci. 2019, 9, 2183.

Journal reference: Appl. Sci. 2019, 9, 2183
DOI: 10.3390/app9112183

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 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 BE patients, using a dataset of images acquired with VLE in a multicenter study. We achieve values of AUC=$0.96$ on the unseen test dataset and we compare our results with previous work done with VLE analysis. 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.

Subject Areas

Barrett's esophagus; deep learning; volumetric laser endomicroscopy; optical coherence tomography; classification; esophageal adenocarcinoma; glands; machine learning

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