Analyzing Social Network Images with Deep Learning Models to Fight Zika Virus

Pedro H. Barros; Bruno G.C. Lima; Felipe C. Crispim; Tiago Vieira; Paolo Missier; Baldoino Fonseca

doi:10.1007/978-3-319-93000-8_69

Analyzing Social Network Images with Deep Learning Models to Fight Zika Virus

Pedro H. Barros, Bruno G.C. Lima, Felipe C. Crispim, Tiago Vieira^*, Paolo Missier, Baldoino Fonseca

^*Corresponding author for this work

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Zika and Dengue are viral diseases transmitted by infected mosquitoes (Aedes aegypti) found in warm, humid environments. Mining data from social networks helps to find locations with highest density of reported cases. Differently from approaches that process text from social networks, we present a new strategy that analyzes Instagram images. We use two customized Deep Neural Networks. The first detects objects commonly used for mosquito reproduction with 85% precision. The second differentiates mosquitoes as Culex or Aedes aegypti with 82.5% accuracy. Results indicate that both networks can improve the effectiveness of current social network mining strategies such as the VazaZika project.

Original language	English
Title of host publication	Image Analysis and Recognition - 15th International Conference, ICIAR 2018, Proceedings
Editors	Bart ter Haar Romeny, Fakhri Karray, Aurelio Campilho
Publisher	Springer Verlag
Pages	605-610
Number of pages	6
ISBN (Print)	9783319929996
DOIs	https://doi.org/10.1007/978-3-319-93000-8_69
Publication status	Published - 2018
Event	15th International Conference on Image Analysis and Recognition, ICIAR 2018 - Povoa de Varzim, Portugal Duration: 27 Jun 2018 → 29 Jun 2018

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10882 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	15th International Conference on Image Analysis and Recognition, ICIAR 2018
Country/Territory	Portugal
City	Povoa de Varzim
Period	27/06/18 → 29/06/18

Bibliographical note

Funding Information:
Acknowledgments. The authors would like to thank: (1) National Council for Scientific and Technological Development (CNPq, grant 447336/2014-2). (2) Deep Learning program provided by the Nervana Academy (Intel©R). (3) FAPEAL grant 60030 1201/2016.

Publisher Copyright:
© 2018, Springer International Publishing AG, part of Springer Nature.

Keywords

Aedes aegypti
Deep Neural Networks
Social networks
Zika

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-319-93000-8_69

Cite this

Barros, P. H., Lima, B. G. C., Crispim, F. C., Vieira, T., Missier, P., & Fonseca, B. (2018). Analyzing Social Network Images with Deep Learning Models to Fight Zika Virus. In B. ter Haar Romeny, F. Karray, & A. Campilho (Eds.), Image Analysis and Recognition - 15th International Conference, ICIAR 2018, Proceedings (pp. 605-610). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10882 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-93000-8_69

Barros, Pedro H. ; Lima, Bruno G.C. ; Crispim, Felipe C. et al. / Analyzing Social Network Images with Deep Learning Models to Fight Zika Virus. Image Analysis and Recognition - 15th International Conference, ICIAR 2018, Proceedings. editor / Bart ter Haar Romeny ; Fakhri Karray ; Aurelio Campilho. Springer Verlag, 2018. pp. 605-610 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{2888aff75244412e873afec16e389918,

title = "Analyzing Social Network Images with Deep Learning Models to Fight Zika Virus",

abstract = "Zika and Dengue are viral diseases transmitted by infected mosquitoes (Aedes aegypti) found in warm, humid environments. Mining data from social networks helps to find locations with highest density of reported cases. Differently from approaches that process text from social networks, we present a new strategy that analyzes Instagram images. We use two customized Deep Neural Networks. The first detects objects commonly used for mosquito reproduction with 85% precision. The second differentiates mosquitoes as Culex or Aedes aegypti with 82.5% accuracy. Results indicate that both networks can improve the effectiveness of current social network mining strategies such as the VazaZika project.",

keywords = "Aedes aegypti, Deep Neural Networks, Social networks, Zika",

author = "Barros, {Pedro H.} and Lima, {Bruno G.C.} and Crispim, {Felipe C.} and Tiago Vieira and Paolo Missier and Baldoino Fonseca",

note = "Funding Information: Acknowledgments. The authors would like to thank: (1) National Council for Scientific and Technological Development (CNPq, grant 447336/2014-2). (2) Deep Learning program provided by the Nervana Academy (Intel{\textcopyright}R). (3) FAPEAL grant 60030 1201/2016. Publisher Copyright: {\textcopyright} 2018, Springer International Publishing AG, part of Springer Nature.; 15th International Conference on Image Analysis and Recognition, ICIAR 2018 ; Conference date: 27-06-2018 Through 29-06-2018",

year = "2018",

doi = "10.1007/978-3-319-93000-8_69",

language = "English",

isbn = "9783319929996",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "605--610",

editor = "{ter Haar Romeny}, Bart and Fakhri Karray and Aurelio Campilho",

booktitle = "Image Analysis and Recognition - 15th International Conference, ICIAR 2018, Proceedings",

}

Barros, PH, Lima, BGC, Crispim, FC, Vieira, T, Missier, P & Fonseca, B 2018, Analyzing Social Network Images with Deep Learning Models to Fight Zika Virus. in B ter Haar Romeny, F Karray & A Campilho (eds), Image Analysis and Recognition - 15th International Conference, ICIAR 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10882 LNCS, Springer Verlag, pp. 605-610, 15th International Conference on Image Analysis and Recognition, ICIAR 2018, Povoa de Varzim, Portugal, 27/06/18. https://doi.org/10.1007/978-3-319-93000-8_69

Analyzing Social Network Images with Deep Learning Models to Fight Zika Virus. / Barros, Pedro H.; Lima, Bruno G.C.; Crispim, Felipe C. et al.
Image Analysis and Recognition - 15th International Conference, ICIAR 2018, Proceedings. ed. / Bart ter Haar Romeny; Fakhri Karray; Aurelio Campilho. Springer Verlag, 2018. p. 605-610 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10882 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Analyzing Social Network Images with Deep Learning Models to Fight Zika Virus

AU - Barros, Pedro H.

AU - Lima, Bruno G.C.

AU - Crispim, Felipe C.

AU - Vieira, Tiago

AU - Missier, Paolo

AU - Fonseca, Baldoino

N1 - Funding Information: Acknowledgments. The authors would like to thank: (1) National Council for Scientific and Technological Development (CNPq, grant 447336/2014-2). (2) Deep Learning program provided by the Nervana Academy (Intel©R). (3) FAPEAL grant 60030 1201/2016. Publisher Copyright: © 2018, Springer International Publishing AG, part of Springer Nature.

PY - 2018

Y1 - 2018

N2 - Zika and Dengue are viral diseases transmitted by infected mosquitoes (Aedes aegypti) found in warm, humid environments. Mining data from social networks helps to find locations with highest density of reported cases. Differently from approaches that process text from social networks, we present a new strategy that analyzes Instagram images. We use two customized Deep Neural Networks. The first detects objects commonly used for mosquito reproduction with 85% precision. The second differentiates mosquitoes as Culex or Aedes aegypti with 82.5% accuracy. Results indicate that both networks can improve the effectiveness of current social network mining strategies such as the VazaZika project.

AB - Zika and Dengue are viral diseases transmitted by infected mosquitoes (Aedes aegypti) found in warm, humid environments. Mining data from social networks helps to find locations with highest density of reported cases. Differently from approaches that process text from social networks, we present a new strategy that analyzes Instagram images. We use two customized Deep Neural Networks. The first detects objects commonly used for mosquito reproduction with 85% precision. The second differentiates mosquitoes as Culex or Aedes aegypti with 82.5% accuracy. Results indicate that both networks can improve the effectiveness of current social network mining strategies such as the VazaZika project.

KW - Aedes aegypti

KW - Deep Neural Networks

KW - Social networks

KW - Zika

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DO - 10.1007/978-3-319-93000-8_69

M3 - Conference contribution

AN - SCOPUS:85049478400

SN - 9783319929996

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 605

EP - 610

BT - Image Analysis and Recognition - 15th International Conference, ICIAR 2018, Proceedings

A2 - ter Haar Romeny, Bart

A2 - Karray, Fakhri

A2 - Campilho, Aurelio

PB - Springer Verlag

T2 - 15th International Conference on Image Analysis and Recognition, ICIAR 2018

Y2 - 27 June 2018 through 29 June 2018

ER -

Barros PH, Lima BGC, Crispim FC, Vieira T, Missier P, Fonseca B. Analyzing Social Network Images with Deep Learning Models to Fight Zika Virus. In ter Haar Romeny B, Karray F, Campilho A, editors, Image Analysis and Recognition - 15th International Conference, ICIAR 2018, Proceedings. Springer Verlag. 2018. p. 605-610. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-93000-8_69

Analyzing Social Network Images with Deep Learning Models to Fight Zika Virus

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

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Cite this