Classification of Optical Coherence Tomography using Convolutional Neural Networks

A. A. Saraiva, A. A. Saraiva, D. B. S. Santos, Pimentel Pedro, Jose Vigno Moura Sousa, N. M. Fonseca Ferreira, N. M. Fonseca Ferreira, J. E. S. Batista Neto, Salviano Soares, Antonio Valente, Antonio Valente

2020

Abstract

This article describes a classification model of optical coherence tomography images using convolution neural network. The dataset used was the Labeled Optical Coherence Tomography provided by (Kermany et al., 2018) with a total of 84495 images, with 4 classes: normal, drusen, diabetic macular edema and choroidal neovascularization. To evaluate the generalization capacity of the models k-fold cross-validation was used. The classification models were shown to be efficient, and as a result an average accuracy of 94.35% was obtained.

Paper Citation

in Harvard Style

Saraiva A., B. S. Santos D., Pedro P., Sousa J., Ferreira N., Neto J., Soares S. and Valente A. (2020). Classification of Optical Coherence Tomography using Convolutional Neural Networks. In Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - Volume 3: BIOINFORMATICS; ISBN 978-989-758-398-8, SciTePress, pages 168-175. DOI: 10.5220/0009091001680175

in Bibtex Style

@conference{bioinformatics20,
author={A. A. Saraiva and D. B. S. B. S. Santos and Pimentel Pedro and Jose Vigno Moura Sousa and N. M. Fonseca Ferreira and J. E. S. Batista Neto and Salviano Soares and Antonio Valente},
title={Classification of Optical Coherence Tomography using Convolutional Neural Networks},
booktitle={Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - Volume 3: BIOINFORMATICS},
year={2020},
pages={168-175},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009091001680175},
isbn={978-989-758-398-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - Volume 3: BIOINFORMATICS
TI - Classification of Optical Coherence Tomography using Convolutional Neural Networks
SN - 978-989-758-398-8
AU - Saraiva A.
AU - B. S. Santos D.
AU - Pedro P.
AU - Sousa J.
AU - Ferreira N.
AU - Neto J.
AU - Soares S.
AU - Valente A.
PY - 2020
SP - 168
EP - 175
DO - 10.5220/0009091001680175
PB - SciTePress