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Authors: A. A. Saraiva 1 ; 2 ; D. B. S. B. S. Santos 3 ; Pimentel Pedro 3 ; Jose Vigno Moura Sousa 3 ; N. M. Fonseca Ferreira 4 ; 5 ; J. E. S. Batista Neto 1 ; Salviano Soares 4 and Antonio Valente 6 ; 2

Affiliations: 1 University of São Paulo, São Carlos, Brazil ; 2 University of Trás-os-Montes and Alto Douro,Vila Real, Portugal ; 3 UESPI - University of State Piaui, Piripiri, Brazil ; 4 Coimbra Polytechnic, ISEC, Coimbra, Portugal ; 5 Knowledge Engineering and Decision-Support Research Center (GECAD) of the Institute of Engineering, Polytechnic Institute of Porto, Portugal ; 6 INESC-TEC Technology and Science, Porto, Portugal

Keyword(s): OCT, CNN, Classification, K-fol, Labeled Optical Coherence Tomography.

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.

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Paper citation in several formats:
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) - BIOINFORMATICS; ISBN 978-989-758-398-8; ISSN 2184-4305, SciTePress, pages 168-175. DOI: 10.5220/0009091001680175

@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) - BIOINFORMATICS},
year={2020},
pages={168-175},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009091001680175},
isbn={978-989-758-398-8},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - BIOINFORMATICS
TI - Classification of Optical Coherence Tomography using Convolutional Neural Networks
SN - 978-989-758-398-8
IS - 2184-4305
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

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