Classification of Images of Childhood Pneumonia using Convolutional Neural Networks

A. A. Saraiva; N. M. Fonseca Ferreira; N. M. Fonseca Ferreira; N. M. Fonseca Ferreira; Luciano Lopes de Sousa; Nator Junior C. Junior C. Costa; José Vigno Moura Sousa; José Vigno Moura Sousa; D. B. S. B. S. Santos; Antonio Valente; Antonio Valente; Salviano Soares

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Paper

Classification of Images of Childhood Pneumonia using Convolutional Neural Networks

Topics: Medical Imaging and Diagnosis

In Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: BIOSTEC, 112-119, 2019 , Prague, Czech Republic

Authors: A. A. Saraiva ¹ ; N. M. Fonseca Ferreira ^{2

;

3

;

4} ; Luciano Lopes de Sousa ⁵ ; Nator Junior C. Junior C. Costa ⁵ ; José Vigno Moura Sousa ^{5

;

6} ; D. B. S. B. S. Santos ⁵ ; Antonio Valente ^{2

;

7} and Salviano Soares ⁷

Affiliations: ¹ UTAD University, Coimbra, Portugal ; ² INESC-TEC Technology and Science, Campus da FEUP, Rua Dr. Roberto Frias 378, 4200 - 465 Porto, Portugal ; ³ Knowledge Engineering and Decision-Support Research Center (GECAD) of the Institute of Engineering, Polytechnic Institute of Porto, Portugal ; ⁴ Department of Electrical Engineering, Institute of Engineering of Coimbra, Polytechnic Institute, Rua Pedro Nunes, 3031-601 Coimbra, Portugal ; ⁵ State University of Piauí, Piauí, Brazil ; ⁶ University Brazil, São Paulo, Brazil ; ⁷ IEETA-UA and School of Science and Technology, University of Trás-os-Montes and Alto Douro, Vila Real, Portugal

Keyword(s): Pneumonia, X-Ray, CNN, K-Fold.

Abstract: In this paper we describe a comparative classification of Pneumonia using Convolution Neural Network. The database used was the dataset Labeled Optical Coherence Tomography (OCT) and Chest X-Ray Images for Classification made available by (Kermany, 2018) with a total of 5863 images, with 2 classes: normal and pneumonia. To evaluate the generalization capacity of the models, cross-validation of k-fold was used. The classification models proved to be efficient compared to the work of (Kermany et al., 2018) which obtained 92.8 % and the present work had an average accuracy of 95.30 %.

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Paper citation in several formats:

Saraiva, A.; Ferreira, N.; Lopes de Sousa, L.; Junior C. Costa, N.; Sousa, J.; B. S. Santos, D.; Valente, A. and Soares, S. (2019). Classification of Images of Childhood Pneumonia using Convolutional Neural Networks. In Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2019) - BIOIMAGING; ISBN 978-989-758-353-7; ISSN 2184-4305, SciTePress, pages 112-119. DOI: 10.5220/0007404301120119

@conference{bioimaging19,
author={A. A. Saraiva. and N. M. Fonseca Ferreira. and Luciano {Lopes de Sousa}. and Nator Junior C. {Junior C. Costa}. and José Vigno Moura Sousa. and D. B. S. {B. S. Santos}. and Antonio Valente. and Salviano Soares.},
title={Classification of Images of Childhood Pneumonia using Convolutional Neural Networks},
booktitle={Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2019) - BIOIMAGING},
year={2019},
pages={112-119},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007404301120119},
isbn={978-989-758-353-7},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2019) - BIOIMAGING
TI - Classification of Images of Childhood Pneumonia using Convolutional Neural Networks
SN - 978-989-758-353-7
IS - 2184-4305
AU - Saraiva, A.
AU - Ferreira, N.
AU - Lopes de Sousa, L.
AU - Junior C. Costa, N.
AU - Sousa, J.
AU - B. S. Santos, D.
AU - Valente, A.
AU - Soares, S.
PY - 2019
SP - 112
EP - 119
DO - 10.5220/0007404301120119
PB - SciTePress