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. A., Ferreira, N. M. F., Lopes de Sousa, L., Junior C. Costa, N. J. C., Sousa, J. V. M., B. S. Santos, D. B. S., 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