Classification of Images of Childhood Pneumonia using Convolutional Neural Networks

被引：47

作者：

Saraiva, A. A. ^{[1
]}

Fonseca Ferreira, N. M. ^{[2
,3
,4
]}

de Sousa, Luciano Lopes ^{[5
]}

Costa, Nator Junior C. ^{[5
]}

Moura Sousa, Jose Vigno ^{[5
,6
]}

Santos, D. B. S. ^{[5
]}

Valente, Antonio ^{[2
,7
,8
]}

Soares, Salviano ^{[7
,8
]}

机构：

[1] UTAD Univ, Coimbra, Portugal

[2] INESC TEC Technol & Sci, Campus FEUP,Rua Dr Roberto Frias 378, P-4200465 Porto, Portugal

[3] Polytech Inst Porto, Inst Engn, Knowledge Engn & Decis Support Res Ctr GECAD, Porto, Portugal

[4] Polytech Inst, Inst Engn Coimbra, Dept Elect Engn, Rua Pedro Nunes, P-3031601 Coimbra, Portugal

[5] Univ Estadual Piaui, Piaui, Brazil

[6] Univ Brazil, Sao Paulo, Brazil

[7] Univ Tras Os Montes & Alto Douro, IEETA UA, Vila Real, Portugal

[8] Univ Tras Os Montes & Alto Douro, Sch Sci & Technol, Vila Real, Portugal

来源：

BIOIMAGING: PROCEEDINGS OF THE 12TH INTERNATIONAL JOINT CONFERENCE ON BIOMEDICAL ENGINEERING SYSTEMS AND TECHNOLOGIES, VOL 2 | 2019年

关键词：

Pneumonia; X-Ray; CNN; K-Fold;

D O I：

10.5220/0007404301120119

中图分类号：

R318 [生物医学工程];

学科分类号：

0831 ;

摘要：

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 %.

引用

页码：112 / 119

页数：8

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