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Authors: A. Saraiva 1 ; D. Santos 2 ; Nator Costa 2 ; Jose Sousa 3 ; N. Ferreira 4 ; Antonio Valente 5 and Salviano Soares 6

Affiliations: 1 UESPI-University of State Piauí, Piripiri, Brazil, School of Science and Technology, University of Trás-os-Montes and Alto Douro, Vila Real and Portugal ; 2 UESPI-University of State Piauí, Piripiri and Brazil ; 3 University Brazil, São Paulo, Brazil, UESPI-University of State Piauí, Piripiri and Brazil ; 4 Department of Electrical Engineering, Institute of Engineering of Coimbra, Coimbra, Polytechnic Institute, Portugal, Knowledge Engineering and Decision-Support Research Center (GECAD) of the Institute of Engineering, Polytechnic Institute of Porto and Portugal ; 5 School of Science and Technology, University of Trás-os-Montes and Alto Douro, Vila Real, Portugal, NESC-TEC Technology and Science, Campus da FEUP, Rua Dr. Roberto Frias 378, 4200-465 Porto and Portugal ; 6 University of Trás-os-Montes and Alto Douro, Vila Real and Portugal

ISBN: 978-989-758-353-7

Keyword(s): Pneumonia, CNN, MLP, Classification, k-Fold, Chest-X-Ray.

Related Ontology Subjects/Areas/Topics: Bioimaging ; Biomedical Engineering ; Medical Imaging and Diagnosis

Abstract: This article describes a comparison of two neural networks, the multilayer perceptron and Neural Network, for the detection and classification of pneumonia. The database used was the Chest-X-Ray data set provided by (Kermany et al., 2018) with a total of 5840 images, with two classes, normal and with pneumonia. to validate the models used, cross-validation of k-fold was used. The classification models were efficient, resulting in an average accuracy of 92.16% with the Multilayer Perceptron and 94.40% with the Convolution Neural Network.

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Paper citation in several formats:
Saraiva, A.; Santos, D.; Costa, N.; Sousa, J.; Ferreira, N.; Valente, A. and Soares, S. (2019). Models of Learning to Classify X-ray Images for the Detection of Pneumonia using Neural Networks.In Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2 BIOIMAGING: BIOIMAGING, ISBN 978-989-758-353-7, pages 76-83. DOI: 10.5220/0007346600760083

@conference{bioimaging19,
author={A. A. Saraiva. and D. B. S. Santos. and Nator Junior C. Costa. and Jose Vigno M. Sousa. and N. M. Fonseca Ferreira. and Antonio Valente. and Salviano Soares.},
title={Models of Learning to Classify X-ray Images for the Detection of Pneumonia using Neural Networks},
booktitle={Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2 BIOIMAGING: BIOIMAGING,},
year={2019},
pages={76-83},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007346600760083},
isbn={978-989-758-353-7},
}

TY - CONF

JO - Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2 BIOIMAGING: BIOIMAGING,
TI - Models of Learning to Classify X-ray Images for the Detection of Pneumonia using Neural Networks
SN - 978-989-758-353-7
AU - Saraiva, A.
AU - Santos, D.
AU - Costa, N.
AU - Sousa, J.
AU - Ferreira, N.
AU - Valente, A.
AU - Soares, S.
PY - 2019
SP - 76
EP - 83
DO - 10.5220/0007346600760083

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